>> Announcer: From around the globe, it's theCUBE with digital coverage of AWS reInvent 2020 sponsored by Intel, AWS and our community partners. >> Hi, and welcome to theCUBE virtual and our coverage of AWS reInvent 2020. I'm your host Rebecca Knight. Joining me is Sachin Dhoot, he is the vice president for data and platform engineering at Ellie Mae. Thank you so much for coming on theCUBE, Sachin. >> Nice to be here. >> So we are talking today about Ellie Mae's journey towards data monetization. Before we begin though, I want you to give our viewers a little bit, tell our viewers a little bit about yourself and your role at Ellie Mae. >> Sure. So I'm the vice president for data and platform engineering at Ellie Mae. A little bit about Ellie Mae before I talk about myself. So Ellie Mae, which is now part of ICE Mortgage Technology, a division of Intercontinental exchange is the leading cloud based loan origination platform for the mortgage industry. Our technology solutions actually enable lenders to originate more loans, lower origination cost and reduce the time to close. Or when ensuring the highest degree of compliance quality and efficiency. Our mission as we call it here internally is to automate everything 'automatable' for the residential mortgage industry. So that's what we do here. And we take great pride in doing that. >> Everything automatable, I love it. >> Yes. And if you have gone through the mortgage process, you'll see the number of papers you have to sign. And so we are on the journey to automate as much as possible in this. So as part of this, my charter here so I'm the vice president of data and platform engineering. Like I said, I lead and I'm responsible for all AWS based platform and data solutions including our highly secure, scalable data platform and the global, literally. Just to give you a magnitude of how much data we are talking about; so currently Ellie Mae in its platform stores data of nearly 50% of all US for mortgages. So that's the scale which we are talking about and I'm responsible for having the AWS based data platform to support that. >> So in terms of the data monetization journey like most innovations, it starts with a problem. What was the problem that you were trying to solve here? >> Yes, that's a great question. So earlier in our initial design what used to happen is the customers had access to their loan origination system and data in it. And the way they had access to the data was writing some customer SDK applications to actually export our data from their production systems. So this had its own share of challenges. Like for example, if I wrote some inefficient queries to export out the data, since they were acting on the same production database it used to slow down their loan origination system. Plus they did not get access to all of their data. And we had heard it loud and clear from our customers that not only did they need access to the data, but they also wanted us to manage their data. They did not want to get into managing the database or schema changes and all of that. Plus we also had such a rich industry data set. We are talking about 50% of all US home mortgages. So they were also very interested in using that data to get actionable insights about the industry, about their competitive advantages and develop some innovative services on top of it. So those were the challenges which we were trying to solve. >> So what was the original architecture like you're describing what sounds like a very poor experience for Ellie Mae and the lenders themselves. It sounds clunky and cumbersome. And then also leaving a lot on the table because as you said, it was a rich dataset. What was the original architecture? >> So the original architecture was not a cloud-based architecture. We were in our own private data center and every customer had their own database to work with. So, and it wasn't great architecture at that time when the technologies had not evolved. And we had a highly successful product as a result of that but when it came to data it was not a very good experience for them. So why did their loan origination system was working great? The access to the data was not to the extent what we wanted. >> So using best-in-class technologies from AWS tell us a little bit about the new product. >> Yes. So, our journey really started when we heard all of the customer's feedback and the requirements. Then we basically went back to the drawing board. We said, yes, we have a highly successful encompass product in the market, but we also want to solve this problem without affecting their experience with the loan origination system. So that was the challenge which we had taken internally. So what we did was we evaluated quite a bit of cloud providers and technology stacks and the parameters which we had put in that time because of the scale of data was, we needed unlimited scalability and reliability of any provider. We needed a secure data storage including the personally identifiable information protection. So as you can imagine, we deal with loan mortgages, I mean the mortgage and we pretty much have so much of PII data as we call it. Security is on the forefront for us. So we needed a cloud provider which could match up with that expectation. We needed.. >> AWS, was it? >> AWS was definitely it and there were some other parameters which also we were able to check because of that highly scalable and performance data Lake. We needed a big data Lake for this, storage compute separation. We also needed ability to seamlessly import data from any applications internal or external, right? And AWS absolutely gave us all of this. And we did evaluate a lot of cloud vendors and AWS came up on the top. So AWS along with persistent technologies actually helped us with this evaluation and the development of the data platform. >> So tell our viewers a little bit now about data connect and what it is for lenders now. >> Yeah. So what we did was as any cloud technology, we first developed a common platform and then we started building data connect solutions on top of it, right? So we created solutions based on the customer's needs. So one solution which we have is what we call as the data connects future products. In this, they can replicate, customers can replicate their data from the cloud, from their private data Lake into their warehouse, or they can access reports and run analytical queries directly on our warehouse which is again in the cloud. So all the solutions that are available depending on the customer's needs but that is all separate from the loan origination system. So we made sure that we are not impacting that existing business while creating this new solutions in the market. And all of these were built on AWS. >> But you also took things a step further and explored what was possible if you aggregated data from all lenders the resulting being insights. Tell our viewers a little bit about insights and what it allows. >> Absolutely. So that was a very cool product which we came up with. So again, because of the rich data set, which we have, right? We are in the position right now to aggregate the data and come up with actionable insights on top of the data. And so we call this product insights. This is our latest offering from Ellie Mae, again based off AWS and the data platform. So this product gives us information about the industry dreams on how the mortgage industry is going in US. It gives the lenders the ability to compare themselves with their peers and with the industry. So they can actually benchmark themselves and decide whether they are doing great, not great, what do they have to change? And this is all in near real time. So this is not like a month old data and all that. So that's the beauty of this product. >> And what are you hearing from customers? Because as you said, that real-time benchmarking and understanding how they're doing relative to their rivals is a game changer. It is and customers are super excited about it. We just launched this few months back and we are seeing amazing adoption for this product. In fact, just not the adoption side of things, we are also seeing so many new use cases and requirements coming from the customer now that they understand we have such a massive data and this data can scale and it's not impacted their business. They just want to add more and more things to it so that it can solve their problem. So it gives a unique opportunity for us where we can monetize more but we can also help solve lenders problems. >> Right. Helping them solve the challenges that they're facing. Talk a little bit more about the primary benefits of the solution, the unlimited scalability, the fact that it's fully managed, the storage compute separation. Tell our viewers a little bit more about the benefits. So the benefits about the solutions are, the customers or lenders don't have to worry about how it is managed. It is all taken care of. They just how to access it when they need it. It is available on demand. It is available 24/7. In this time, this year has been especially very busy for us where the interest rates have dropped and the loan volume and the loan applications have just gone through the roof. But I'm very proud to say that Ellie Mae stack or, all of the data solutions, and in fact, all of our other products, they are able to scale and they have been able to scale to the record volume this year, all because of how we have designed it using the AWS technology stack. So the customers really benefit. They just need to focus on their business. They don't have to worry about underlying infrastructure or how things are going to scale if their volume is going to go up or not or is there any security issues of that? We take care of all of those things and this is all a self provision just web based access for some of our products. So they don't even have to do a lot of customization to get hold of these products. >> So I want to ask what's next for you. You just referenced the fact that Ellie Mae's incredibly busy with record mortgage applications, of course, companies and people around the globe are still grappling with the COVID-19 pandemic. What are some of the big trends you're seeing and what's next for Ellie Mae in the coming coming year? >> We have a exciting and a very rich roadmap coming up. So as I started this interview, I said, Ellie Mae is now part of ICE mortgage technology, which is a Intercontinental exchange division. So as part of this transition, which happened recently, we also have under our umbrella, two companies called MERS and Simplifile, which actually touch so if you take MERS as an example, it touches close to 80% of US loans for home mortgages. So we have such a unique opportunity now to not only expand our data set, make it more rich, and then come up with more additional use cases which are going to help solve customer's problem and also make them competitive in the market. So we have a lot of good opportunity related to data and I feel a lot confident because of the data platform and the technology stack we to use. We will be able to handle all of those things. >> Sachin, tell our viewers a little bit about the partners that are helping you on this data monetization journey. >> So AWS definitely helped us in the initial parts in evaluating the design and the solution architects came in and worked with us. But along with that, I would definitely want to mention Persistent Technologies. They came up with a lot of good design suggestions on how we should develop the data platform and the solutions on top of it. Those insights product, which I talked about is done along with their help. So I'm very happy with the partnership I have with the Persistent Technologies and AWS. >> Excellent, well, Sachin Dhoot, thank you so much for coming on theCUBE. I really appreciate talking to you >> Same here, nice talking to you. >> Stay tuned for more of theCUBE virtual coverage at AWS reInvent. (upbeat music)