(upbeat music) >> Hey everyone. Welcome back to the show floor in Las Vegas Snowflake Summit 22 with 7,000 plus folks here, Lisa Martin with Dave Vellante. Great to be back in person. We're excited to welcome a couple of guests that join us next. Persona Christian is here. The director of product for collaboration and Snowflake marketplace. Katie Laughlin joins us as well. The Global Head Offerings, Human Data Science Cloud at Customer IQVIA. Ladies, welcome to the program. >> Thank you. >> Thank you for having us. >> Dave: All right. Thanks for coming on. >> Katie, let's go ahead and start with you. Give the audience an overview of IQVIA. What you guys do, your mission, what you deliver? >> Yeah, sure. So, IQVIA is a healthcare focused data analytics and clinical research organization. We have 82,000 employees. We operate in a hundred countries and we have tens of thousands of data deliverables that we curate for our customers and deliver to them on a monthly basis. So, we're 100% healthcare focused, whether it's clinical research, helping our customers support their clinical trials, real world evidence, how are medicines operating in the market or commercial aspects. You know, how is your company performing overall in the market? >> How long have you been a customer of Snowflake's? >> A few years. Yeah. >> A few years, okay. Persona, tremendous growth going on right now. There's a rocket ship. You could even feel kind of like the whiplash from the keynote and all the announcements going on, but looking at the first quarter 23, fiscal 23 results, product revenue, 384 million, 85% growth tremendous momentum going on, big growth in customers. Talk to us about IQVIA, its partnership with Snowflake and the data driver award program. They, they just won. >> Yeah, absolutely. I'll start with a little bit about the Snowflake collaboration capabilities, which enable these thousands of customers to really collaborate on the data cloud to be able to break down silos between data and drive business decisions based on data and applications that live outside your own four walls as well. And this is where IQVIA, as a leader in healthcare data, bringing together data to enable healthcare organizations to be more data driven and to really drive insights. One, the data for good award, which we are really excited with for the partnership and really excited to have IQVIA be the winner of the award. >> And what does that mean? The data for good. We always love talking about that, Katie. >> Katie: Sure. What does that mean? How is that embodied at IQVIA? >> Can you say the last part? >> Yeah. How is that embodied at IQVIA? >> That's a great question. I think everyone that works at IQVIA believes in the mission, which is really to drive healthcare forward. We're really proud of a lot of the things that we do. So, with the advent of COVID, for example, we really had to pivot and help our customers. How do we keep executing on clinical trials? We supported a lot of the COVID trials that came forward and helped our customers understand how is this affecting patients in the real world? And how is it affecting your commercial operations? So, being in Vegas with tens of thousands of people around and almost nobody wearing masks, I think to myself, I'm part of the organization an organization that helped make that possible. >> So Frank Slootman today, Katie talked about compress. He talked about one pharmaceutical compressing from nine years to seven years, you guys have done a lot of obviously contract research over the years. So, what has that Snowflake journey been like? What's been the business impact of of working with that and the collaboration? >> Yeah. So my focus is really around our data as a service offering, which is where we're enabling our customers to ingest their data in modern ways. So if you imagine, you know, we've done everything from paper to big tapes of data for over 60 years of of our company being in business, now to VPN, SFTP, making multiple hops of data from one end to the other. I was just learning about one of our use cases where we're able to cut down processing time for our customers for two weeks. They data share some data with us. We do some additional processing on that. We serve it back to them and we're saving them two weeks of time to gain time to insights. >> Right. And Prasanna, collaboration transcends data sharing, right? It's almost like it's, that's, that's sort of the the first, the core of the concentric circle, right? >> Prasanna: Yeah. >> Talk about what else is embodied in collaboration. >> Yeah, that's a great question. So the first problem that we solved was getting access to data through our core sharing technology. And as you were talking about Katie, replacing FTPs and having to build APIs, which were cumbersome, and instead being able to access data on the data cloud without having to copy or move anything. That was the core sharing technology. But that solves the first problem, which is the access problem. The second problem is how do I discover what what's out there? How do I better understand it? How do I evaluate it? How do I try it and buy it? And those are all the problems that we're solving with the marketplace, which is now home to both data and applications that you can discover, try, and buy. >> Katie, talk to us about what IQVIA was doing before Snowflake? What was that life like before? How were you enabling customers to leverage data to make data driven decisions? >> Yeah, so we, as I said, we're a data and analytics company. So we provide some native analytics capabilities to our customers, but most customers, most of the large customers I would say, they're building their own data lakes. They have their own ecosystems. Some of them are adopting Snowflake and we really needed to partner with them on being able to get the data to them as quickly as possible. So like, I, I was just describing a minute ago we would have multiple hops where we deliver to a location, customer ingests it, customer does their QC. Then they process it and then it appears in their data warehouse. And now we're able to adopt their QC protocols within our own platform and deliver the data to them much more quickly. >> And what does that enable to your business from an outcomes perspective? If you look at overall Snowflake as an engine what is it enabling and empowering IQVIA to accomplish? >> So it helps us partner with our customers in modern ways. So I'm saying we've been in the data business for 60 years. So it's sometimes it's a legacy behemoth that you need to bring along to modern times. And I think for us, the shift has been night and day in terms of Snowflake's capabilities. >> So you will build data based apps in the Snowflake data cloud? Is that, is that where you're headed? >> Yes. So we have several applications that we built natively on Snowflake that we offer to our customers. >> And what will that bring you that you kind of couldn't do before? >> That we couldn't do before? I think the the ability to, we talk a lot about how you spend 80% of your time cooking the data, right? Getting it ready for insights and only 20% of your time being able to to bring those insights forward and Snowflake, it really helps us flip that ratio so that we don't have to worry so much about the scaling and the infrastructure and the data sourcing. We can focus more on driving those insights and innovations. >> So Prasanna, we talk a lot about, you have this application stack over here and it sends a database over here and then you have an analytics stack. It seems like you're enabling those worlds to come together. Is that, is that by design? Is that more organic? Can you talk about that? >> Yeah. I mean, that is essential to our our mission and our value prop is to bring it together. It's one product, it's seamless and lets you do more with your data. Benoit talked today in the opening keynote about running multiple workloads on your data and the way you do that is by having one product that allows you to to run your data, data queries but also build applications that can run against that data. >> Katie, can you share a little bit about the partnership? We'll say collaboration that IQVIA has with Snowflake in terms of your ability to influence the roadmap in the direction. We heard a lot of customer stories in the keynote and they talked a lot about Frank Slootman did, Benoit, Christian. We are listening to our customers. Do you feel that as a, a customer for the last few years? >> Yeah, absolutely. So we have a really broad partnership with Snowflake. We're a customer. We have OEM licensing where we're building applications on top of Snowflake. We're an SI partner where we're marrying our data healthcare expertise along with Snowflake technology expertise and helping customers build and utilize the data internally and as well as just, if nothing else, the Snowflake data share in order to deliver the data into their environment. >> Prasanna, what do you look for in a data driver winner? Like what stood out about IQVIA and others that aspire to that, what should they be focused on? >> Yeah, I mean, you know, we ultimately think that in every business you have business needs that you're trying to solve and business is inherently collaborative. You never solve problems with just what you have within your own four walls. And IQVIA is an example of someone that's really enabling outcomes for healthcare companies to be much faster through live access to data. Which is what we want to accomplish for the data cloud, help our company, help our customers solve business needs. >> Every company has to be a data company these days, right? There's no, you have no choice. We talked about, you know, software eating the world a few years. Now we're talking about data eating the world. For organizations, it's in any any vertical healthcare, life sciences, retail, finance. It's essential to not just have data, live data access to it, to be able to extract insights from it that you can act on. Talk about what you are doing at Snowflake as a differentiator? Is that goal of becoming the defacto standard data platform and what that enables partners like IQVIA to accomplish? >> Yeah. It starts with our fundamental architecture, which allows you to collaborate and access data without creating copies of it or sending around copies and built on top of that now, the ability to build applications and to monetize them really enables our customers to do more with their data and to monetize it and to be able to distribute it without having to deal with all the plumbing. >> That's nice. That saves you a lot of time. What do you think when you, Katie, if you talk to people that are your peers in either healthcare or other industries, what are like the top couple of recommendations that you would have for them? We have a data problem. It's all a data problem. How do we actually leverage value from this fast so we can be competitive? >> Yeah. So I think if I were to advise someone who is thinking about commercializing their data set, when if they haven't before, you know, you have to think about good data governance protocols, good data cataloging. Make sure you're, you know, conforming to all of the privacy rules that you need to and overseeing the management of that data, any changes in the data, you know, delivering that both to internal and external customers. But I think, just a quick plug for Snowflake, what I would say on a personal level is that their partner first mentality really is a pleasure, makes it a pleasure to work with them and makes it really easy for us to enable our services through, through Snowflake. >> Frank Slootman talked about mission alignment this morning, kind of a mission I thought of, of aligning on with the missions of their customers and partners. It sounds like that's what Katie's talking about from a cultural perspective. You've got that alignment here? >> Yes, absolutely. You know, we work with our partners to enable our customers to drive business value and solve the needs of their industry. >> What are some of the things that you are excited about? Fourth Annual Summit. We, I, I said 7,000 plus people we'll get numbers kind of later on. What are you excited about finally being back in person? >> Yes, of course. >> Being able to access this hugely growing population of customers and partners, what excites you about this Summit 22? >> What excites me most is the fact that we are now enabling our customers to do more, to build applications which has been a big theme at Summit, but also to be able to distribute and monetize this. So as Frank talked about this morning, helping customers drive value and more value from, from their data. >> Critical. Katie, last question for you. If we look at all the,it was a very technical keynote this morning. You talked about the great partnership, the synergies the alignment that IQVIA has with Snowflake. What are you excited about in terms of hearing and seeing and feeling and touching this week at Summit? >> Well, yesterday we won an award for Data Marketplace. Marketplace Partner of the year for healthcare and life sciences. That was really exciting for us. It was great recognition for us in terms of how we've been able to modernize on the cloud. But I'm really excited to see how much the Snowflake business has grown as well. Our General Manager for information management was telling me, he said, when I come to this conference a couple of years ago it was only a few thousand people and now it's really, it's really grown and really taken off. And it's really exciting to see how many of the different partnerships are interacting and and that we're able to take advantage of as well. >> Yeah, I think we heard earlier this morning that the first summit four years ago was a couple thousand people. Now here we are eight, eight to ten. We've also seen, Persona, I mentioned some of the product revenue numbers for fiscal 23 Q1. I also noticed that in the last four years, the number percentage of customers with a million plus ARR is grown over 1200%. Number of customers is growing, the high value customers are growing. It seems like you're on a rocket ship here with Snowflake. Would you agree? >> Yeah. We're excited with all the value that we're bringing to our customers and the growth we're seeing. >> Dave: Yeah. Way to amp it up. >> Yeah, absolutely. >> Excellent. Ladies, thank you so much for joining us talking about the partnership with IQVIA and Snowflake. Congratulations again. >> Katie: Thank you. >> Katie, on IQVIA winning the data driver award, Data for good >> Great to hear what you're doing together and how you're enabling organizations in the healthcare industry to maximize the value of data. We appreciate your insights. >> Thank you. >> Dave: Thank you guys. >> Thanks. >> For our guests, Dave Vellante, I'm Lisa Martin. You're watching the Cube's live coverage from Las Vegas of Snowflake Summit 22. Stick around, Dave and I will be right back with our next guest.

>> Okay, we continue now with the theme of turning ideas into insights so ultimately you can take action. We heard earlier that public cloud first doesn't mean public cloud only. And a winning strategy comprises data, irrespective of physical location on prem, across multiple clouds at the edge where real-time inference is going to drive a lot of incremental value. Data is going to help the world come back to normal we heard, or at least semi normal as we begin to better understand and forecast demand and supply imbalances and economic forces. AI is becoming embedded into every aspect of our business, our people, our processings, and applications. And now we're going to get into some of the foundational principles that support the data and insights centric processes, which are fundamental to digital transformation initiatives. And it's my pleasure to welcome two great guests, Michelle Goetz, who's a Cube alum and VP and principal analyst at Forrester, and doin' some groundbreaking work in this area. And Cindy Maike who is the vice president of industry solutions and value management at Cloudera. Welcome to both of you. >> Welcome, thank you. >> Thanks Dave. >> All right Michelle, let's get into it. Maybe you could talk about your foundational core principles. You start with data. What are the important aspects of this first principle that are achievable today? >> It's really about democratization. If you can't make your data accessible, it's not usable. Nobody's able to understand what's happening in the business and they don't understand what insights can be gained or what are the signals that are occurring that are going to help them with decisions, create stronger value or create deeper relationships with their customers due to their experiences. So it really begins with how do you make data available and bring it to where the consumer of the data is rather than trying to hunt and peck around within your ecosystem to find what it is that's important. >> Great thank you for that. So, Cindy, I wonder in hearing what Michelle just said, what are your thoughts on this? And when you work with customers at Cloudera, are there any that stand out that perhaps embody the fundamentals that Michelle just shared? >> Yeah, there's quite a few. And especially as we look across all the industries that were actually working with customers in. A few that stand out in top of mind for me is one is IQVIA. And what they're doing with real-world evidence and bringing together data across the entire healthcare and life sciences ecosystems, bringing it together in different shapes and formats, making it accessible by both internally, as well as for the entire extended ecosystem. And then for SIA, who's working to solve some predictive maintenance issues within, they're are a European car manufacturer and how do they make sure that they have efficient and effective processes when it comes to fixing equipment and so forth. And then also there's an Indonesian based telecommunications company, Techsomel, who's bringing together over the last five years, all their data about their customers and how do they enhance a customer experience, how do they make information accessible, especially in these pandemic and post pandemic times. Just getting better insights into what customers need and when do they need it? >> Cindy, platform is another core principle. How should we be thinking about data platforms in this day and age? Where do things like hybrid fit in? What's Cloudera's point of view here? >> Platforms are truly an enabler. And data needs to be accessible in many different fashions, and also what's right for the business. When I want it in a cost and efficient and effective manner. So, data resides everywhere, data is developed and it's brought together. So you need to be able to balance both real time, our batch, historical information. It all depends upon what your analytical workloads are and what types of analytical methods you're going to use to drive those business insights. So putting in placing data, landing it, making it accessible, analyzing it, needs to be done in any accessible platform, whether it be a public cloud doing it on-prem or a hybrid of the two is typically what we're seeing being the most successful. >> Great, thank you. Michelle let's move on a little bit and talk about practices and processes, the next core principles. Maybe you could provide some insight as to how you think about balancing practices and processes while at the same time managing agility. >> Yeah, it's a really great question 'cause it's pretty complex when you have to start to connect your data to your business. The first thing to really gravitate towards is what are you trying to do. And what Cindy was describing with those customer examples is that they're all based off of business goals, off of very specific use cases. That helps kind of set the agenda about what is the data and what are the data domains that are important to really understanding and recognizing what's happening within that business activity and the way that you can affect that either in near time or real time, or later on, as you're doing your strategic planning. What that's balancing against is also being able to not only see how that business is evolving, but also be able to go back and say, "Well, can I also measure the outcomes from those processes and using data and using insight? Can I also get intelligence about the data to know that it's actually satisfying my objectives to influence my customers in my market? Or is there some sort of data drift or detraction in my analytic capabilities that are allowing me to be effective in those environments?" But everything else revolves around that and really thinking succinctly about a strategy that isn't just data aware, what data do I have and how do I use it? But coming in more from that business perspective, to then start to be data driven, recognizing that every activity you do from a business perspective leads to thinking about information that supports that and supports your decisions. And ultimately getting to the point of being insight driven, where you're able to both describe what you want your business to be with your data, using analytics to then execute on that fluidly and in real time. And then ultimately bringing that back with linking to business outcomes and doing that in a continuous cycle where you can test and you can learn, you can improve, you can optimize and you can innovate. Because you can see your business as it's happening. And you have the right signals and intelligence that allow you to make great decisions. >> I like how you said near time or real time, because it is a spectrum. And at one end of the spectrum, autonomous vehicles. You've got to make a decision in real time but near real-time, or real-time, it's in the eyes of the beholder if you will. It might be before you lose the customer or before the market changes. So it's really defined on a case by case basis. I wonder Michelle, if you could talk about in working with a number of organizations I see folks, they sometimes get twisted up in understanding the dependencies that technology generally, and the technologies around data specifically can sometimes have on critical business processes. Can you maybe give some guidance as to where customers should start? Where can we find some of the quick wins and high returns? >> It comes first down to how does your business operate? So you're going yo take a look at the business processes and value stream itself. And if you can understand how people, and customers, partners, and automation are driving that step by step approach to your business activities, to realize those business outcomes, it's way easier to start thinking about what is the information necessary to see that particular step in the process, and then take the next step of saying what information is necessary to make a decision at that current point in the process? Or are you collecting information, asking for information that is going to help satisfy a downstream process step or a downstream decision? So constantly making sure that you are mapping out your business processes and activities, aligning your data process to that helps you now rationalize do you need that real time, near real time, or do you want to start creating greater consistency by bringing all of those signals together in a centralized area to eventually oversee the entire operations and outcomes as they happen? It's the process, and the decision points, and acting on those decision points for the best outcome that really determines are you going to move in more of a real-time streaming capacity, or are you going to push back into more of a batch oriented approach? Because it depends on the amount of information and the aggregate of which provides the best insight from that. >> Got it. Let's, bring Cindy back into the conversation here. Cindy, we often talk about people, process, and technology and the roles they play in creating a data strategy that's logical and sound. Can you speak to the broader ecosystem and the importance of creating both internal and external partners within an organization? >> Yeah. And that's kind of building upon what Michelle was talking about. If you think about datas and I hate to use the phrase almost, but the fuel behind the process and how do you actually become insight-driven. And you look at the capabilities that you're needing to enable from that business process, that insight process. Your extended ecosystem on how do I make that happen? Partners and picking the right partner is important because a partner is one that actually helps you implement what your decisions are. So looking for a partner that has the capability that believes in being insight-driven and making sure that when you're leveraging data within your process that if you need to do it in a real-time fashion, that they can actually meet those needs of the business. And enabling on those process activities. So the ecosystem looking at how you look at your vendors, and fundamentally they need to be that trusted partner. Do they bring those same principles of value, of being insight driven? So they have to have those core values themselves in order to help you as a business person enable those capabilities. >> So Cindy I'm cool with fuel, but it's like super fuel when you talk about data. 'Cause it's not scarce, right? You're never going to run out. (Dave chuckling) So Michelle, let's talk about leadership. Who leads? What does so-called leadership look like in an organization that's insight driven? >> So I think the really interesting thing that is starting to evolve as late is that organizations, enterprises are really recognizing that not just that data is an asset and data has value, but exactly what we're talking about here, data really does drive what your business outcomes are going to be. Data driving into the insight or the raw data itself has the ability to set in motion what's going to happen in your business processes and your customer experiences. And so, as you kind of think about that, you're now starting to see your CEO, your CMO, your CRO coming back and saying, I need better data. I need information that's representative of what's happening in my business. I need to be better adaptive to what's going on with my customers. And ultimately that means I need to be smarter and have clearer forecasting into what's about ready to come. Not just one month, two months, three months, or a year from now, but in a week or tomorrow. And so that is having a trickle down effect to then looking at two other types of roles that are elevating from technical capacity to more business capacity. You have your chief data officer that is shaping the experiences with data and with insight and reconciling what type of information is necessary with it within the context of answering these questions and creating a future fit organization that is adaptive and resilient to things that are happening. And you also have a chief digital officer who is participating because they're providing the experience and shaping the information and the way that you're going to interact and execute on those business activities. And either running that autonomously or as part of an assistance for your employees and for your customers. So really to go from not just data aware to data-driven, but ultimately to be insight driven, you're seeing way more participation and leadership at that C-suite level and just underneath, because that's where the subject matter expertise is coming in to know how to create a data strategy that is tightly connected to your business strategy. >> Great, thank you. Let's wrap, and I've got a question for both of you, maybe Cindy, you could start and then Michelle bring us home. A lot of customers, they want to understand what's achievable. So it's helpful to paint a picture of a maturity model. I'd love to go there, but I'm not going to get there anytime soon, but I want to take some baby steps. So when you're performing an analysis on an insight driven organization, Cindy what do you see as the major characteristics that define the differences between sort of the early beginners sort of fat middle, if you will, and then the more advanced constituents? >> Yeah, I'm going to build upon what Michelle was talking about is data as an asset. And I think also being data aware and trying to actually become insight driven. Companies can also have data, and they can have data as a liability. And so when you're data aware, sometimes data can still be a liability to your organization. If you're not making business decisions on the most recent and relevant data, you're not going to be insight-driven. So you've got to move beyond that data awareness, where you're looking at data just from an operational reporting. But data's fundamentally driving the decisions that you make as a business. You're using data in real time. You're leveraging data to actually help you make and drive those decisions. So when we use the term you're data-driven, you can't just use the term tongue-in-cheek. It actually means that I'm using the recent, the relevant, and the accuracy of data to actually make the decisions for me, because we're all advancing upon, we're talking about artificial intelligence and so forth being able to do that. If you're just data aware, I would not be embracing on leveraging artificial intelligence. Because that means I probably haven't embedded data into my processes. Yes, data could very well still be a liability in your organization, so how do you actually make it an asset? >> Yeah I think data aware it's like cable ready. (Dave chuckling) So Michelle, maybe you could add to what Cindy just said and maybe add as well any advice that you have around creating and defining a data strategy. >> So every data strategy has a component of being data aware. This is like building the data museum. How do you capture everything that's available to you? How do you maintain that memory of your business? Bringing in data from your applications, your partners, third parties, wherever that information is available, you want to ensure that you're capturing it and you're managing and you're maintaining it. And this is really where you're starting to think about the fact that it is an asset, it has value. But you may not necessarily know what that value is yet. If you move into a category of data driven, what starts to shift and change there is you're starting to classify label, organize the information in context of how you're making decisions and how you do business. It could start from being more proficient from an analytic purpose. You also might start to introduce some early stages of data science in there. So you can do some predictions and some data mining to start to weed out some of those signals. And you might have some simple types of algorithms that you're deploying to do a next next best action, for example. And that's what data-driven is really about. You're starting to get value out of it. The data itself is starting to make sense in context of your business, but what you haven't done quite yet, which is what insight driven businesses are, is really starting to take away the gap between when you see it, know it, and then get the most value and really exploit what that is at the time when it's right, so in the moment. We talk about this in terms of perishable insights, data and insights are ephemeral. And we want to ensure that the way that we're managing that and delivering on that data and insights is in time with our decisions and the highest value outcome we're going to have, that that insight can provide us. So are we just introducing it as data-driven organizations where we could see spreadsheets and PowerPoint presentations and lots of mapping to help make longer strategic decisions, or are those insights coming up and being activated in an automated fashion within our business processes that are either assisting those human decisions at the point when they're needed, or an automated decisions for the types of digital experiences and capabilities that we're driving in our organization. So it's going from, I'm a data hoarder if I'm data aware to I'm interested in what's happening as a data-driven organization and understanding my data. And then lastly being insight driven is really where light between business, data and insight, there is none, it's all coming together for the best outcomes. >> Right, it's like people are acting on perfect or near perfect information. Or machines are doing so with a high degree of confidence. Great advice and insights, and thank you both for sharing your thoughts with our audience today, it was great to have you. >> Thank you. >> Thank you. >> Okay, now we're going to go into our industry deep dives. There are six industry breakouts. Financial services, insurance, manufacturing, retail communications, and public sector. Now each breakout is going to cover two distinct use cases for a total of essentially 12 really detailed segments. Now each of these is going to be available on demand, but you can scan the calendar on the homepage and navigate to your breakout session of choice. Or for more information, click on the agenda page and take a look to see which session is the best fit for you and then dive in. Join the chat and feel free to ask questions or contribute your knowledge, opinions, and data. Thanks so much for being part of the community, and enjoy the rest of the day. (upbeat music)

(upbeat music) >> It's my pleasure, to be one of the hosts of theCUBE on cloud and the startup showcase brought to you by AWS. This is Dave Vellante and for years theCUBE has been following the trail of data. And with the relentless match of data growth this idea of a single version of the truth has become more and more elusive. Moreover, data has become the lifeblood of a digital business. And if there's one thing that we've learned throughout the pandemic, if you're not digital, you're in trouble. So we've seen firsthand, the critical importance of reliable and trusted data. And with me to talk about his company and the trends in the market is Manish Sood the CTO and co-founder of Reltio. Manish, welcome to the program. >> Thank you, Dave. It's a pleasure to be here. >> Okay, let's start with, let's go back to you and your co-founders when you started Reltio it was back in the early days of the big data movement, cloud was kind of just starting to take off, but what problems did you see then and what are enterprises struggling with today, especially with data as a source of digital innovation. >> Dave, if you look at the changes that have taken place in the landscape over the course of the last 10 years, when we started Reltio in 2011 there were a few secular trends that were coming to life. One was a cloud compute type of capabilities being provided by vendors like AWS. It was starting to pick up steam where making compute capabilities available at scale to solve large data problems was becoming real and possible. The second thing that we saw was this big trend of you know, you can not have a wall to wall, one single application that solves your entire business problem. Those visions have come and gone and we are seeing more of the best of breed application type of a landscape where even if you look within a specific function let's say sales or marketing, you have more than a dozen applications that any company is using today. And that trend was starting to emerge where we knew very well that the number of systems that we would have to work with would continue to increase. And that created a problem of where would you get the single source of truth or the single best origin of a customer, a supplier, a product that you're trying to sell, those types of critical pieces of information that are core to any business that's out there today. And, you know, that created the opportunity for us at Reltio to think about the problem at scale for every company out there, every business who needed this kind of capability and for us to provide this capability in the cloud as a software, as a service offering. So that's where, you know, the foundation of Reltio started. And the core problem that we wanted to solve was to bridge the gap that was created by all these data silos, and create a unified view of the core critical information that these companies run on. >> Yeah, the cloud is this giant, you know hyper distributed system, data by its very nature is distributed. It's interesting what you were sort of implying about you know, the days of the monolithic app are gone, but my business partner years ago John Furrier at theCUBE said, data is going to become the new development kit. And we've certainly seen that with the pandemic but tell us more about Reltio and how you help customers deal with that notion of data silos, data fragmentation, how do you solve that problem? >> So data fragmentation is what exists today. And, with the Reltio software as a service offering that we provide, we allow customers to stitch together and unify the data coming from these different fragmented siloed applications or data sources that they have within their enterprise. At the same time, there's a lot of dependence on the third party data. You know, when you think about different problems that you're trying to solve, you have for B2B type of information that in Bradstreet type of data providers, in life sciences you have IQVIA type of data providers. You know, as you look at other verticals that is a specialized third party data provider for any and every kind of information that most of the enterprise businesses want to combine with their in-house data or first party data to get the best view of who they're dealing with, who are they working with, you know who are the customers that they're serving and use that information also as a starting point for the digital transformation that they want to get to. And that's where Reltio fits in as the only platform that can help stitch together this kind of information and create a 360 degree view that spans all the data silos and provides that for real-time use, for BI and analytics to benefit from, for data science to benefit from, and then this emerging notion of data in itself is a, you know, key starting point that is used by us in order to make any decisions. Just like we go, you know, if I they wanted to look at information about you, I would go to places like LinkedIn, look up the information, and then on my next set of decisions with that information. If somebody wanted to look up information on Reltio they would go to, let's say crunchbase as an example and look up, who are the investors? How much money have we raised? All those details that are available. It's not a CRM system by itself but it is an information application that can aid and assist in the decision-making process as a starting point. And that user experience on top of the data becomes an important vehicle for us to provide as a part of the Reltio platform capabilities. >> Awesome, thank you. And I want to get into the tech, but before we do maybe we just cut to the chase and maybe you can talk about some of the examples of Reltio and action, some of the customers that you can talk about, maybe the industries that are really adopting this. What can you tell us there Manish? >> We work across a few different verticals some of the key verticals that we work in are life sciences and travel and hospitality and financial services, insurance retail, as an example. Those are some of the key verticals for us. But to give you some examples of the type of problems that customers are solving with Reltio as the data unification platform, let's take CarMax as an example,. CarMax is a customer who's in the business of buying used cars, selling used cars servicing those used cars. And then, you know, you as a customer don't just transact with them once, you know, you've had a car for three years you go back and look at what can you trade in that car for? But in order for CarMax to provide a service to you that goes across all the different touch points whether you are visiting them at their store location trying to test drive a car or viewing information about the various vehicles on their website, or just you know, punching in the registration number of your car just to see what is the appraisal from them in terms of how much will they pay for your car. This requires a lot of data behind the scenes for them to provide a seamless journey across all touch points. And the type of information that they use relative for aggregating, unifying, and then making available across all these touch points, is all of the information about the customers, all of the information about the household, you know, the understanding that they are trying to achieve because life events can be buying signals for consumers like you and I, as well as who was the associate who helped you either in the selling of a car, buying of a car, because their business is all about building relationships for the longer term, lifetime value that they want to capture. And in that process, making sure that they're providing continuity of relationship, they need to keep track of that data. And then the vehicle itself, the vehicle that you buy yourself, there is a lot of information in order to price it right, that needs to be gathered from multiple sources. So the continuum of data all the way from consumer to the vehicle is aggregated from multiple sources, unified inside Reltio and then made available through APIs or through other methods and means to the various applications, can be either built on top of that information, or can consume that information in order to better aid and assist the processes, business processes that those applications have to run and to end. >> Well, sounds like we come along, (indistinct). >> I was just going to say that's one example and, you know across other verticals, that are other similar examples of how companies are leveraging, Reltio >> Yeah, so as you say, we've come a long way from simple linear clickstream analysis of a website. I mean, you're talking about really rich information and you know happy to dig into some other examples, but I wonder how does it work? I mean, what's the magic behind it? What's the tech look like? I mean, obviously leveraging AWS, maybe you could talk about how, so, and maybe some of the services there and some of your unique IP. >> Yeah, you know, so the unique opportunity for us when we started in 2011 was really to leverage the power of the cloud. We started building out this capability on top of AWS back in 2011. And, you know, if you think about the problem itself, the problem has been around as long as you have had more than one system to run your business, but the magnitude of the problem has expanded several fold. You know, for example, I have been in this area was responsible for creating some of the previous generation capabilities and most of the friction in those previous generation MDM or master data management type of solutions as the you know, the technical term that is used to refer to this area, was that those systems could not keep pace with the increasing number of sources or the depth and breadth of the information that customers want to capture, whether it is, you know, about a patient or a product or let's say a supplier that you're working with, there is always additional information that you can capture and you know use to better inform the decisions for the next engagement. And that kind of model where the number of sources we're always going to increase the depth and breadth of information was always going to increase. The previous generation systems were not geared to handle that. So we decided that not only would we use add scale compute capabilities in the cloud, with the products like AWS as the backbone, but also solve some of the core problems around how more sources of information can be unified at scale. And then the last mile, which is the ability to consume such rich information just locking it in a data warehouse has been sort of the problem in the past, and you talked about the clickstream analysis. Analytics has a place, but most of the analytics is a real view mirror picture of the, you know, work that you have to do versus everybody that we talk to as a potential customer wanted to solve the problem of what can we do at the point of engagement? How can we influence decisions? So, you know, I'll give you an example. I think everybody's familiar with Quicken loans as the mortgage lender, and in the mortgage lending business, Quicken loans is the customer who's using Reltio as the customer data unification platform behind the scenes. But every interaction that takes place, their goal is that they have a very narrow time vendor, you know anywhere from 10 minutes to about an hour where if somebody expresses an interest in refinancing or getting a mortgage they have to close that business within that hot vendor. The conversion ratios are exponentially better in that hot vendor versus waiting for 48 hours to come back with the answer of what will you be able to refinance your mortgage at? And they've been able to use this notion of real time data where as soon as you come in through the website or if you come in through the rocket mortgage app or you're talking to a broker by calling the 1800 number they are able to triangulate that it's the same person coming from any of these different channels and respond to that person with an offer ASAP so that there is no opportunity for the competition to get in and present you with a better offer. So those are the types of things where the time to conversion or the time to action is being looked at, and everybody's trying to shrink that time down. That ability to respond in real time with the capabilities were sort of the last mile missing out of this equation, which didn't exist with previous generation capabilities, and now customers are able to benefit from that. >> That is an awesome example. I know at firsthand, I'm a customer of Quicken and rocket when you experience that environment, it's totally different, than anything you've ever seen before. So it's helpful to hear you explain like what's behind that because, it's truly disruptive and I'll tell you the other thing that sort of triggered a thought was that we use the word realtime a lot and we try to develop years ago. We said, what does real-time really mean? And the answer we landed on was, before you lose the customer, and that's kind of what you just described. And that is what gives as an example a quick and a real advantage again, having experienced it firsthand. It's pretty, pretty tremendous. So that's a nice reference. So, and the other thing that struck me is, I wanted to ask you how it's different from sort of legacy Master Data Management solutions and you sort of described that they've since to me they've got to take their traditional on-prime stack, rip it out, stick it in the iCloud, it's okay we got our stack in the cloud now. Your technical approach is dramatically different. You had the advantage of having a clean sheet of paper, right? I mean, from a CTO's perspective, what's your take? >> Yeah, the clean sheet of paper is the luxury that we have. You know, having seen this movie before having, you know looked at solving this problem with previous generation technologies, it was really the opportunity to start with a clean sheet of paper and define a cloud native architecture for solving the problem at scale. So just to give you an example, you know, across all of our customers, we are today managing about 6.5 billion consolidated profiles of people, organizations, product, locations, you know, assets, those kinds of details. And these are the types of crown jewels of the business that every business runs on. You know, for example, if you wanted to let's say you're a large company, like, you know, Ford and you wanted to figure out how much business are you doing, whether, you know another large company, because the other large company could be a global organization, could be spread across multiple geographies, could have multiple subsidiaries associated with it. It's been a very difficult to answer to understand what is the total book of business that they have with that other big customer. And, you know, being able to have the right, unified, relevant, ready clean information as the starting point that gives you visibility to that data, and then allows you to run precise analytics on top of that data, or, you know drive any kind of conclusions out of the data science type of algorithms or MLAI algorithms that you're trying to run. You have to have that foundation of clean data to work with in order to get to those answers. >> Nice, and then I had questions on just analysis, it's a SAS model I presume, how is it priced? Do you have a freemium? How do I get started? Maybe you could give us some color on that. >> Yeah, we are a SAS provider. We do everything in the cloud, offer it as a SAS offering for customers to leverage and benefit from. Our pricing is based on the volume of consolidated profiles, and I use the word profiles because this is not the traditional data model, where you have rows, columns, foreign keys. This is a profile of a customer, regardless of attribution or any other details that you want to capture. And you know, that just as an example is what we consider as a profile. So number of consolidated profiles under management is the key vector of pricing. Customers can start small and they can grow from there. We have customers who manage anywhere from a few hundred thousand profiles, you know, off these different types of data domains, customer, patient, provider, product, asset, those types of details, but then they grow and some of the customers HPInc, as a customer, is managing close to 1.5 billion profiles of B2B businesses at a global scale of B2C consumers at global scale. And they continue to expand that footprint as they look at other opportunities to use, the single source of truth capabilities provided by Reltio. >> And, and your relationship with AWS, you're obviously building on top of AWS, you're taking advantage of the cloud native capabilities. Are you in the AWS marketplace? Maybe you could talk about AWS relationship a bit. >> Yeah, AWS has been a key partner for us since the very beginning. We are now on the marketplace. Customers can start with the free version of the product and start to play with the product, understand it better and then move into the paid tier, you know as they bring in more data into Reltio and, you know be also have the partnership with AWS where, you know customers can benefit from the relationship where they are able to use the spend against Reltio to offset the commitment credits that they have for AWS, you know, as a cloud provider. So, you know, we are working closely with AWS on key verticals, like life sciences, travel and hospitality as a starting point. >> Nice, love those credits. Company update, you know, head count, funding, revenue trajectory what kind of metrics are you comfortable sharing? >> So we are currently at about, you know, slightly not at 300 people overall at Reltio. We will grow from 300 to about 400 people this year itself we are, you know, we just put out a press release where we mentioned some of the subscription ARR we finished last year at about $74 million in ARR. And we are looking at crossing the hundred million dollar ARR threshold later this year. So we are on a great growth trajectory and the business is performing really well. And we are looking at working with more customers and helping them solve this, you know, data silo, fragmentation of data problem by having them leverage the Reltio capability at scale across their enterprise. >> That's some impressive growth, congratulations. We're, I'm sure adding hundred people you're hiring all over the place, but where we are some of your priorities? >> So, you know, the, as the business is growing we are spending equally, both on the R and D side of the house investing more there, but at the same time also on our go to market so that we can extend our reach, make sure that more people know about Reltio and can start leveraging the benefit of the technology that we have built on top of AWS. >> Yeah, I mean it sounds like you've obviously nailed product market fit and now you're, you know, scaling the grip, go to market. You moved from CEO into the CTO role. Maybe you could talk about that a little bit. Why, what was prompted that move? >> Problems of luxury, you know, as I like to call them once you know that you're in a great growth trajectory, and the business is performing well, it's all about figuring out ways of, you know making sure that you can drive harder and faster towards that growth milestones that you want to achieve. And, you know, for us, the story is no different. The team has done a wonderful job of making sure that we can build the right platform, you know work towards this opportunity that we see, which by the way they've just to share with you, MDM or Master Data Management has always been underestimated as a, you know, yes there is a problem that needs to be solved but the market sizing was in a, not as clear but some of the most recent estimates from analysts like Gartner, but the, you know, sort of the new incarnation of data unification and Master Data Management at about a $30 billion, yeah, TAM for this market. So with that comes the responsibility that we have to really make sure that we are able to bring this capability to a wide array of customers. And with that, I looked at, you know how could we scale the business faster and have the right team to work help us maximize the opportunity. And that's why, you know, we decided that it was the right point in time for me to bring in somebody who's worked at the stretch of, you know taking a company from just a hundred million dollars in ARR to, you know, half a billion dollars in ARR and doing it at a global scale. So Chris Highland, you know, has had that experience and having him take on the CEO role really puts us on a tremendous path or path to tremendous growth and achieving that with the right team. >> Yeah, and I think I appreciate your comments on the TAM. I love to look at the TAM and to do a lot of TAM analysis. And I think a lot of times when you define the the future TAM based on sort of historical categories, you sometimes under count them. I mean, to me you guys are in the digital business. I mean, the data transformation the company transformation business, I mean that could be order of magnitude even bigger. So I think the future is bright for your company Reltio, Manish and thank you so much for coming on the program. Really appreciate it. >> Well, thanks for having me, really enjoyed it. Thank you. >> Okay, thank you for watching. You're watching theCUBEs Startup Showcase. We'll be right back. (upbeat music)

(upbeat music) >> Live, from Las Vegas it's theCUBE. Covering Informatica World 2019. Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World 2019. I'm your host Rebecca Knight along with my co-host John Furrier. We are joined by Adam Mariano, he is the Vice-President Health Informatics at HighPoint Solutions. Thanks for coming on theCUBE! >> Thank you for having me. >> So tell our viewers a little bit about HighPoint Solutions, what the company does and what you do there. >> Sure, HighPoint is a consulting firm in the Healthcare and Life Sciences spaces. If it's data and it moves we probably can assist with it. We do a lot of data management, we implement the full Infomatica stack. We've been an Infomatica partner for about 13 years, we were their North American partner of the year last year. We're part of a much larger organization, IQVIA, which is a merger of IMS quintiles, large data asset holder, big clinical research organization. So we're very much steeped in the healthcare data space. >> And what do you do there as Vice President of Health and Formatics? >> I'm in an interesting role. Last year I was on the road 51 weeks. So I was at over a hundred facilities, I go out and help our customers or prospective customers or just people we've met in the space, get strategic about how they're going to leverage data as a corporate asset, figure out how they're going to use it for clinical insight, how they're going to use it for operational support in payer spaces. And really think about how they're going to execute on their next strategy for big data, cloud strategy, digital re-imaginment of the health care space and the like. >> So we know that healthcare is one of the industries that has always had so much data, similar to financial services. How are the organizations that you're working with, how are they beginning to wrap their brains around this explosion of data? >> Well it's been an interesting two years, the last augur two years there isn't a single conversation that hasn't started with governance. And so it's been an interesting space for us. We're a big MDM proponent, we're a big quality proponent, and you're seeing folks come back to basics again, which is I need data quality, I need data management from a metadata perspective, I need to really get engaged from a master data management perspective, and they're really looking for integrated metadata and governance process. Healthcare's been late to the game for about five or six years behind other industries. I think now that everybody's sort of gone through meaningful use and digital transformation on some level, we're now arcing towards consumerism. Which really requires a big deep-dive in the data. >> Adam, data governance has been discussed at length in the industry, certainly recently everyone knows GDPR's one year anniversary, et cetera, et cetera. But the role of data is really critical applications for SAS and new kinds of use cases, and the term Data Provisioning as a service has been kicked around. So I'd love to get your take on what that means, what is the definition, what does it mean? Data Provisioning as a service. >> The industry's changed. We've sort of gone through that boomerang, alright, we started deep in the sort of client server, standard warehouse space. Everything was already BMS. We then, everybody moved to appliances, then everybody came back and decided Hadoop, which is now 15 year old technology, was the way to go. Now everybody's drifting to Cloud, and you're trying to figure out how am I going to provision data to all these self-service users who are now in the sort of bring your own tools space. I'd like to use Tablo, I'd like to use Click. I like SAS. People want to write code to build their own data science. How can you provision to all those people, and do so through a standard fashion with the same metadata with the same process? and there isn't a way to do that without some automation at this point. It's really just something you can't scale, without having an integrated data flow. >> And what's the benefits of data provisioning as a service? What's the impact of that, what does it enable? >> So the biggest impact is time to market. So if you think about warehousing projects, historically a six month, year-long project, I can now bring data to people in three weeks. In two days, in a couple of hours. So thinking about how I do ingestion, if you think about the Informatica stack, something like EDC using enterprise data catalog to automatically ingest data, pushing that out into IDQ for quality. Proving that along to AXON for data governance and process and then looking at enterprise data lake for actual self-service provisioning. Allowing users to go in and look at their own data assets like a store, pick things off the shelf, combine them, and then publish them to their favorite tools. That premise is going to have to show up everywhere. It's going to have to show up on AWS, and on Amazon, and on Azure. It's going to have to show up on Google, it's going to have to show up regardless of what tool you're using. And if you're going to scale data science in a real meaningful way without having to stack a bunch of people doing data munging, this is the way it's going to have to go. >> Now you are a former nurse, and you now-- >> I'm still a nurse, technically. >> You're still a nurse! >> Once a nurse, always a nurse. Don't upset the nurses. >> I've got an ear thing going on, can you help me out here? (laughter) >> So you have this really unique vantage point, in the sense that you are helping these organizations do a better job with their data, and you also have a deep understanding of what it's like to be the medical personnel on the other side, who has to really implement these changes, and these changes will really change how they get their jobs done. How would you say, how does that change the way you think about what you do? And then also what would you say are the biggest differences for the nurses that are on the floor today, in the hospital serving patients? >> I think, in America we think about healthcare we often talked about Doctors, we only talk about nurses in nursing shortages. Nurses deliver all the care. Physicians see at this point, the way that medicine is running, physicians see patients an average two to four minutes. You really think about what that translates to if you're not doing a surgery on somebody, it's enough time to talk to them about their problem, look at their chart and leave. And so nursing care is the point of care, we have a lot of opportunity to create deflection and how care is delivered. I can change quality outcomes, I can change safety problems, I can change length of stay, by impacting how long people keep IVs in after they're no longer being used. And so understanding the way nursing care is delivered, and the lack of transparency that exists with EMR systems, and analytics, there's an opportunity for us to really create an open space for nursing quality. So we're talking a lot now to chief nursing officers, who are never a target of analytics discussion. They don't necessarily have the budget to do a lot of these things, but they're the people who have the biggest point of control and change in the way care is delivered in a hospital system. >> Care is also driven by notifications and data. >> Absolutely. >> So you can't go in a hospital without hearing all kinds of beeps and things. In AI and all the things we've been hearing there's now so many signals, the question is what they pay attention to? >> Exactly. >> This becomes a really interesting thing, because you can get notifications, if everything's instrumented, this is where kind of machine learning, and understanding workflows, outcomes play a big part. This is the theme of the show. It's not just the data and coding, it's what are you looking for? What's the problem statement or what's the outcome or scenario where you want the right notification, at the right time or a resource, is the operating room open? Maybe get someone in. These kinds of new dynamics are enabled by data, what's your take on all this? >> I think you've got some interesting things going on, there's a lot of signal to noise ratio in healthcare. Everybody is trying to build an algorithm for something. Whether that's who's going to overstay their visit, who's going to be readmitted, what's the risk for somebody developing sepsis? Who's likely to follow up on a pharmacy refill for their medication? We're getting into the space where you're going to have to start to accept correlation as opposed to causation, right? We don't have time to wait around for a six month study, or a three year study where you employ 15,000 patients. I've got three years of history, I've got a current census for the last year. I want to figure out, when do I have the biggest risk for falls in a hospital unit? Low staffing, early in their career physicians and nurses? High use of psychotropic meds? There are things that, if you've been in the space, you can pretty much figure out which should go into the algorithm. And then being pragmatic about what data hospitals can actually bring in to use as part of that process. >> So what you're getting at is really domain expertise is just as valuable as coding and wrangling data, and engineering data. >> In healthcare if you don't have SMEs you're not going to get anything practical done. And so we take a lot of these solutions, as one of the interesting touch points of our organization, I think it's where we shine, is bringing that subject matter expertise into a space where pure technology is not going to get it done. It's great if you know how to do MDM. But if you don't know how to do MDM in healthcare, you're going to miss all the critical use cases. So it really - being able to engage that user base, and the SMEs and bring people like nurses to the forefront of the conversation around analytics and how data will be used to your point, which signals to pay attention to. It's critical. >> Supply chains, another big one. >> Yeah. >> Impact there? >> Well it's the new domain in MDM. It's the one that was ignored for a long time. I think people had a hard time seeing the value. It's funny I spoke at 10 o'clock today, about supply chain, that was the session that I had with Nathan Rayne from BJC. We've been helping them embark on their supply chain journey. And from all the studies you look at it's one of the easiest places to find ROI with MBM. There's an unbelievable amount of ways- >> Low hanging fruit. >> $24.5 billion in waste a year in supply chain. It's just astronomical. And it's really easy things, it's about just in time supplies, am I overstocking, am I losing critical supplies for tissue samples, that cost sometimes a $100,000, because a room has been delayed. And therefore that tissue sits out, it ends up expiring, it has to be thrown away. I'll bring up Nathan's name again, but he speaks to a use case that we talked about, which is they needed a supply at a hospital within the system, 30 miles away another hospital had that supply. The supply costs $40,000. You can only buy them in packs of six. The hospital that needed the supply was unaware that one existed in the system, they ordered a new pack of six. So you have a $240,000 price that you could have resolved with a $100 Uber ride, right? And so the reality is that supply could have been shipped, could have been used, but because that wasn't automated and because there was no awareness you couldn't leverage that. Those use cases abound. You can get into the length of stay, you can get into quality of safety, there's a lot of great places to create wins with supply chain in the MDM space. >> One of the conversations we're having a lot in theCUBE, and we're having here at Informatica World, it centers around the skills gap. And you have a interesting perspective on this, because you are also a civil rights attorney who is helping underserved people with their H1B visas. Can you talk a little bit about the visa situation, and what you're seeing particularly as it relates to the skills gap? >> We're in an odd time. We'll leave it at that. I won't make a lot of commentary. >> Yes. >> I'm a civil rights and immigration attorney, and on the immigration side I do a lot of pro bono work with primarily communities of color, but communities at risk looking to help adjust their immigration status. And what you've had is a lot of fear. And so you have, well you might have an H1B holder here, you may have somebody who's on a provisional visa, or family members, and because those family members can no longer come over, people are going home. And you're getting people who are now returning. So we're seeing a negative immigration of places like Mexico, you're seeing a lot of people take their money, and their learnings and go back to India and start companies there and work remotely. So we're seeing a big up-tick in people who are looking for staffing again. I think the last quarter or so has been a pretty big ramp-up. And I think there's going to continue to be this hole, we're going to have to find new sources of talent if we can't bring people in to do the jobs. We're still also, I think it just speaks to our STEM education the fact that we're not teaching kids. I have a 28 year old daughter who loves technology, but I can tell you, her education when she was a kid, was lacking in this technology space. I think it's really an opportunity for us to think about how do we train young people to be in the new data economy. There's certainly an opportunity there today. >> And what about the, I mean you said you were talking about your daughter's education. What would you have directed her toward? What kinds of, when you look ahead to the jobs of the future, particularly having had various careers yourself, what would you say the kids today should be studying? >> That's two questions. So my daughter, I told her do what makes you happy. But I also made her learn Sequel. >> Be happy, but learn Sequel. >> But learn sequel. >> Okay! >> And for kids today I would say look, if you have an affinity and you think you enjoy the computer space, so you think about coding, you like HTML, you like social media. There are a plethora of jobs in that space and none of them require you to be an architect. You can be a BA, you can be a quality assurance person, you can be a PM. You can do analysis work. You can do data design, you can do interface design, there's a lot of space in there. I think we often reject kids who don't go to college, or don't have that opportunity. I think there's an opportunity for us to reach down into urban centers and really think about how we make alternate pathways for kids to get into the space. I think all the academies out there, you're seeing rise, Udemy, and a of of these other places that are offering academy based programs that are three, six months long and they're placing all of their students into jobs. So I don't think that the arc that we've always chased which is you've got to come from a brand named school to get into the space, I don't think it's that important. I think what's important is can I get you the clinical skill, so that you've understood how to move data around, how to process it, how to do testing, how to do design, and then I can bring you into the space and bring you in as an entry level employee. That premise I think is not part of the American dream but it should be. >> Absolutely, looking for talent in these unexpected places. >> College is not the only in point. We're back to having I think vocational schools for the new data economy, which don't exist yet. That's an opportunity for sure. >> And you said earlier, domain expertise, in healthcare as an example, points to what we've been hearing here at the conference, is that with data understanding outcomes and value of the data actually is just as important, as standing up, wrangling data, because if you don't have the data-- >> You make a great point. The other thing I tell young people in my practice, young people I interact with, people who are new to the space is, okay I hear you want to be a data scientist. Learn the business. So if you don't know healthcare get a healthcare education. Come be on this project as a BA. I know you don't want to be a BA, that's fine. Get over it. But come be here and learn the business, learn the dialogue, learn the economy of the business, learn who the players are, learn how data moves through the space, learn what is the actual business about. What does delivering care actually look like? If you're on the payer side, what does claims processing look like from an end to end perspective? Once you understand that I can put you in any role. >> And you know digital four's new non-linear ways to learn, we've got video, I see young kids on YouTube, you can learn anything now. >> Absolutely. >> And scale up your learning at a pace and if you get stuck you can just keep getting through it no-- >> And there are free courses everywhere at this point. Google has a lot of free courses, Amazon will let you train for free on their platform. It's really an opportunity-- >> I think you're right about vocational specialism is actually a positive trend. You know look at the college University scandals these days, is it really worth it? (laughter) >> I got my nursing license through a vocational school originally. But the nursing school, they didn't have any technology at that point. >> But you're a great use case. (laughter) Excellent Adam, thank you so much for coming on theCUBE it's been a pleasure talking to you. >> Thank you. >> I'm Rebecca Knight for John Furrier. You are watching theCUBE. (techno music)