(upbeat music) >> Welcome back to theCUBE's continuing coverage of Snowflake Summit 22 live from Caesars Forum in Las Vegas. I'm Lisa Martin with Dave Vellante. We've got a couple of guests joining us now. We're going to be talking about financial services. Rinesh Patel joins us, the Global Head of Financial Services for Snowflake, and Jack Berkowitz, Chief Data Officer at ADP. Guys, welcome to the program. >> Thanks, thanks for having us. >> Thanks for having us. >> Talk to us about what's going on in the financial services industry as a whole. Obviously, we've seen so much change in the last couple of years. What does the data experience look like for internal folks and of course, for those end user consumers and clients? >> So, one of the big things happening inside of the financial services industry is overcoming the COVID wait, right? A lot of banks, a lot of institutions like ours had a lot of stuff on-prem. And then the move to the Cloud allows us to have that flexibility to deal with it. And out of that is also all these new capabilities. So the machine learning revolution has really hit the services industry, right? And so it's affecting how our IT teams or our data teams are building applications. Also really affecting what the end consumers get out of them. And so there's all sorts of consumerization of the experience over the past couple of years much faster than we ever expected it to happen. >> Right, we have these expectations as consumers that bleed into our business lives that I can do transactions. It's going to be on the swipe in terms of checking authenticity, fraud detection, et cetera. And of course we don't want things to go back in terms of how brands are serving us. Talk about some of the things that you guys have put in place with Snowflake in the last couple of years, particularly at ADP. >> Yeah, so one of the big things that we've done, is, one of the things that we provide is compensation data. So we issue a thing called the National Employment Report that informs the world as to what's happening in the U.S. economy in terms of workers. And then we have compensation data on top of that. So the thing that we've been able to do with Snowflake is to lower the time that it takes us to process that and get that information out into the fingertips of people. And so people can use it to see what's changed in terms of with the worker changes, how much people are making. And they can get it very, very quickly. And we're able to do that with Snowflake now. Used to take us weeks, now it's in a matter of moments we can get that updated information out to people. >> Interesting. It helps with the talent war and- >> Helps in the talent war, helps people adjust, even where they're going to put supply chain in reaction to where people are migrating. We can have all of that inside of the Snowflake system and available almost instantaneously. >> You guys announced the Financial Data Cloud last year. What was that like? 'Cause I know we had Frank on early, he clearly was driving the verticalization of Snowflake if you will, which is kind of rare for a relatively new software company but what's that been like? Give us the update on where you're at and biggest vertical, right? >> Absolutely, it's been an exciting 12 months. We're a platform, but the journey and the vision is more. We're trying to bring together a fragmented ecosystem across financial services. The aim is really to bring together key customers, key data providers, key solution providers all across the different Clouds that exist to allow them to collaborate with data in a seamless way. To solve industry problems. To solve industry problems like ESG, to solve industry problems like quantitative research. And we're seeing a massive groundswell of customers coming to Snowflake, looking at the Financial Services Data Cloud now to actually solve business problems, business critical problems. That's really driving a lot of change in terms of how they operate, in terms of how they win customers, mitigate risk and so forth. >> Jack, I think, I feel like the only industry that's sometimes more complicated than security, is data. Maybe not, security's still maybe more fragmented- >> Well really the intersection of the two is a nightmare. >> And so as you look out on this ecosystem, how do you as the chief data officer, how do you and your organization, what process do you use to decide, okay, which of the, like a chef, which of these ingredients am I going to put together for my business. >> It's a great question, right? There's been explosion of companies. We kind of look at it in two ways. One is we want to make sure that the software and the data can interoperate because we don't want to be in the business of writing bridge code. So first thing is, is having the ecosystem so that the things are tested and can work together. The other area is, and it's important to us is understanding the risk profile of that company. We process about 20% of the U.S. payroll, another 25% of the taxes. And so there's a risk to us that we have an imperative to protect. So we're looking at those companies are they financed, what's their management team. What's the sales experience like, that's important to us. And so technology and the experience of the company coming together are super important to us. >> What's your purview as a chief data officer, I mean, a lot of CDOs that I know came out of the back office and it was a compliance or data quality. You come out of industry from a technology company. So you're sort of the modern... You're like the modern CDO. >> Thanks. Thanks. >> Dave: What's your role? >> I appreciate that. >> You know what I'm saying though? >> And for a while it was like, oh yeah, compliance. >> So I actually- >> And then all of a sudden, boom, big deal. >> Yeah, I really have two jobs. So I have that job with data governance but a lot of data security. But I also have a product development unit, a massive business in monetization of data or people analytics or these compensation benchmarks or helping people get mortgages. So providing that information, so that people can get their mortgage, or their bank loans, or all this other type of transactional data. *So it's both sides of that equation is my reading inside. >> You're responsible for building data products? >> That's right. >> Directly. >> That's right. I've got a massive team that builds data products. >> Okay. That's somewhat unique in your... >> I think it's where CDOs need to be. So we build data products. We build, and we assist as a hub to allow other business units to build analytics that help them either optimize their cost or increase their sales. And then we help with all that governance and communication, we don't want to divide it up. There's a continuum to it. >> And you're a peer of the CIO and the CISO? >> Yeah, exactly. They're my peers. I actually talk to them almost every day. So I've got the CIO as a peer. >> It's a team. >> I've got the security as a peer and we get things done together. >> Talk about the alignment with business. We've been talking a lot about alignment with the data folks, the business folks, the technical folks to identify the right solutions, to be able to govern data, to monetize it, to create data products. What does that... You mentioned a couple of your cohorts, but on the business side, who are some of those key folks? >> So we're like any other big, big organization. We have lots of different business units. So we work directly with either the operational team or the heads of those business units to divine analytic missions that they'll actually execute. And at the same time, we actually have a business unit that's all around data monetization. And so I work with them every single day. And so these business units will come together. I think the big thing for us is to define value and measure that value as we go. As long as we're measuring that value as we go, then we can continue to see improvements. And so, like I said, sometimes it's bottom line, sometimes it's top line, but we're involved. Data is actually a substrate of the company. It's not a side thing to the company. >> Yeah, you are. >> ADP. >> Yeah but if they say data first but you really are data first. >> Yeah. I mean, our CEO says- >> Data's your product. >> Data's our middle name. And it literally is. >> Well, so what do you do in the Snowflake financial services data Cloud? Are you monetizing? >> Yeah. >> What's the plan? >> Yeah, so we have clients. So part of our data monetization is actually providing aggregate and anonymized information that helps other clients make business decisions. So they'll take it into their analytics. So, supply chain optimization, where should we actually put the warehouses based on the population shifts? And so we're actually using the file distribution capabilities or the information distribution, no longer files, where we use Snowflake to actually be that data cloud for those clients. So the data just pops up for our other clients. >> I think the industry's existed a lot with the physical movement of data. When you physically move data, you also physically move the data management challenges. Where do you store it? How do you map it? How do you concord it? And ultimately data sharing is taking away that friction that exists. So it's easier to be able to make informed decisions with the data at hand across two counterparties. >> Yeah, and there's a benefit to us 'cause it lowers our friction. We can have a conversation and somebody can be... Obviously the contracts have to be signed, but once they get done, somebody's up and running on it within minutes. And where it used to be, as you were saying, the movement of data and loss of control, we never actually lose control of it. We know where it is. >> Or yeah, contracts signed, now you got to go through this long process of making sure everything's cool, or a lot of times it could slow down the sale. >> That's right. >> Let's see how that's going to... Let's do a little advanced work. Now you're working without a contract. Here, you can say, "Hey, we're in the Snowflake data cloud. It's governed, you're a part of the ecosystem." >> Yeah, and the ecosystem we announced, oh gee, I think it's probably almost a year and a half ago, a relationship with ICE, Intercontinental Exchange, where they're actually taking our information and their information and creating a new data product that they in turn sell. So you get this sort of combination. >> Absolutely. The ability to form partnerships and monetize data with your partners vastly increases as a consequence. >> Talk to us about the adoption of the financial services data cloud in the last what, maybe nine months or so, since it was announced? And also in terms of the its value proposition, how does the ADP use case articulate that? >> So, very much so. So in terms of momentum, we're a global organization, as you mentioned, we are verticalized. So we have increasingly more expertise and expertise experience now within financial services that allows us to really engage and accelerate our momentum with the top banks, with the biggest asset managers by AUM, insurance companies, sovereign wealth funds on Snowflake. And obviously those data providers and solution providers that we engage with. So the momentum's really there. We're really moving very, very fast in a great market because we've got great opportunity with the capabilities that we have. I mean, ADP is just one of many use cases that we're working with and collaborations that we're taking to market. So yeah, the opportunity to monetize data and help our partners monetize the data has vastly increased within this space. >> When you think about... Oh go ahead, please. >> Yeah I was just going to say, and from our perspective, as we were getting into this, Snowflake was with us on the journey. And that's been a big deal. >> So when you think about data privacy, governance, et cetera, and public policy, it seems like you have, obviously you got things going on in Europe, and you got California, you have other states, there's increasing in complexity. You guys probably love that. (Dave laughs) More data warehouses, but where are we at with that whole? >> It's a great question. Privacy is... We hold some of the most critical information about people because that's our job to help people get paid. And we respect that as sort of our prime agenda. Part of it deals with the technology. How do you monitor, how do you see, make sure that you comply with all these regulations, but a lot of it has to do with the basic ethics of why you're doing and what you're doing. So we have a data and AI ethics board that meets and reviews our use cases. Make sure not only are we doing things properly to the regulation, but are these the types of products, are these the types of opportunities that we as a company want to stand behind on behalf of the consumers? Our company's been around 75 years. We talk about ourselves as a national asset. We have a trust relationship. We want to ensure that that trust relationship is never violated. >> Are you in a position where you can influence public policy and create more standards or framework. >> We actually are, right. We issue something every month called the National Employment Report. It actually tells you what's happening in the U.S. economy. We also issue it in some overseas countries like France. Because of that, we work a lot with various groups. And we can help shape, either data policy, we're involved in understanding although we don't necessarily want to be out in the front, but we want to learn about what's happening with federal trade commission, EOC, because at the end of the day we serve people, I always joke ADP, it's my grandfather's ADP. Well, it was actually my grandfather's ADP. (Dave laughs) He was a small businessman, and he used a ADP all those years ago. So we want to be part of that conversation because we want to continue to earn that trust every day. >> Well, plus your observation space is pretty wide. >> And you've got context and perspective on that that you can bring. >> We move somewhere between two, two and a half trillion dollars a year through our systems. And so we understand what's happening in the economy. >> What are some of the, oh sorry. >> Can your National Employment Report combined with a little Snowflake magic tell us what the hell's going to happen with this economy? >> It's really interesting you say that. Yeah, we actually can. >> Okay. (panelists laugh) >> I think when you think about the amount of data that we are working with, the types of partners that we're working with, the opportunities are infinite. They really, really are. >> So it's either a magic eight ball or it's a crystal ball, but you have it. >> We think- >> We've just uncovered that here on theCUBE. >> We think we have great partners. We have great data. We have a set of industry problems out there that we're working, collaboration with the community to be able to solve. >> What are some of the upcoming use cases Rinesh, that excite you, that are coming up in financial services- >> Great question. >> That snowflake is just going to knock out of the park. >> So look, I think there's a set of here and now problems that the industry faces, ESG's a good one. If you think about ESG, it means many different things from business ethics, to diversity, to your carbon footprint and every asset manager has to make sure they have now some form of green strategy that reflects the values of their investors. And every bank is looking to put in place sustainable lending to help their corporate customers transition. That's a big data problem. And so we're very much at the center of helping those organizations support those informed investors and help those corporates transition to a more sustainable landscape. >> Let me give you an example on Snowflake, we launched capabilities about diversity benchmarks. The first time in the industry companies can understand for their industry, their size, their location what their diversity profile looks like and their org chart profile looks like to differentiate or at least to understand are they doing the right things inside the business. The ability for banks to understand that and everything else, it's a big deal. And that was built on Snowflake. >> I think it's massive, especially in the context of the question around regulation 'cause we're seeing more and more disclosure agreements come out where regulators are making sure that there's no greenwashing taking place. So when you have really strong sources of data that are standardized, that allow that investment process to ingest that data, it does allow for a better outcome for investors. >> Real data, I mean, that diversity example they don't have to rely on a survey. >> It's not a survey. >> Anecdotes. >> It's coming right out of the transactional systems and it's updated, whenever those paychecks are run, whether it's weekly, whether it's biweekly or monthly, all that information gets updated and it's available. >> So it sounds like ADP is a facilitator of a lot of companies ESG initiatives, at least in part? >> Well, we partner with companies all the time. We have over 900,000 clients and all of them are... We've never spoken to a client who's not concerned about their people. And that's just good business. And so, yeah we're involved in that and we'll see where it goes over time now. >> I think there's tremendous opportunity if you think about the data that the ADP have in terms of diversity, in terms of gender pay gap. Huge, huge opportunity to incorporate that, as I said into the ESG principles and criteria. >> Good, 'cause that definitely is what needs to be addressed. (Lisa laughs) Guys thank you so much for joining Dave and me on the program, talking about Snowflake ADP, what you're doing together, and the massive potential that you're helping unlock with the value of data. We appreciate your insights and your time. >> Thank you for having us. >> Dave: Thanks guys. >> Thank you so much. >> For our guests, and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, live in Las Vegas at Snowflake Summit 22. Dave and I will be right back with our next guest. (upbeat music)

>> 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)