>> Okay, listen, we're gearing up for the start of the Snowflake Data Cloud Summit and we want to go back to the early roots of Snowflake. We got some of the founding engineers here, Abdul Muneer, Ashish Modivala, and Alison Lee. They're three individuals that were at Snowflake in the early years and participated in many of the technical decisions that led to the platform that is making Snowflake famous today. Folks, great to see you. Thanks so much for taking some time out of your busy schedules. >> Than you for having us. >> Same. >> Hey, it's got to be really gratifying to see this platform that you've built, you know, taking off and changing businesses. So, I'm sure it was always smooth sailing, right? There were no debates, were there ever? >> Never. >> Now, I've never seen an engineer get into a debate. (laughter) >> All right, so seriously though, so take us back to the early days, you guys, you know, choose whoever wants to start but, what was it like early on? We're talking 2013 here, right? >> That's right. >> When I think back to the early days of Snowflake, I just think of all of us sitting in one room at the time you know, we just had an office that was one room with you know, 12 or 13 engineers sitting there, clacking away at our keyboards, working really hard, churning out code, punctuated by, you know, somebody asking a question about, "Hey, what should we do about this? Or what should we do about that?" And then everyone kind of looking up from their keyboards and getting into discussions and debates about, about the work that we were doing. >> So Abdul, it was just kind of heads down, headphones on, just coding, or >> I think there was a lot of talking and followed by a lot of typing. And, and I think there were periods of time where, you know, anyone could just walk in into the office and probably out of the office and all they'd hear is probably people typing away at their keyboards. And one of my vivid, most vivid memories is is actually I used to sit right across from Alison and there's these huge two, two huge monitors monitors between us. And I would just hear her typing away at our keyboard. And sometimes I was thinking and and all that typing got me nervous because it seemed like Alison knew exactly what, what she needed to do, and I was just still thinking about it. >> So Ashish was this like bliss for you as a developer, an engineer, or was it, was it a stressful time? What was the mood? >> When you don't have a whole lot of customers there's a lot of bliss, but at the same time, there's a lot of pressure on us to make sure that we build the product. There was a timeline ahead of us, we knew we had to build this in a certain timeframe. So one thing I'll add to what Alison and Abdul said is we did a lot of white boarding as well. There were a lot of discussions and those discussions were a lot of fun. They actually cemented what we wanted to build. They made sure that everyone was in tune and there we have it. >> (Dave) Yeah, so, I mean, it is a really exciting time doing any startup. When you have to make decisions in development and variably you come to a fork in the road. So I'm curious as to what some of those forks might've been, how you guys decided, you know, which fork to take. Was there a Yoda in the room that served as the Jedi master? I mean, how are those decisions made? Maybe you could talk about that a little bit. >> Yeah. That's an interesting question. And I think one of, as I think back, one of the memories that, that sticks out in my mind is this epic meeting in one of our conference rooms called North star. And many of our conference rooms are named after ski resorts because the founders are really into skiing and that's why, that's where the Snowflake names comes from. So there was this epic meeting and and I'm not even sure exactly what topic we were discussing. I think it was, it was the signup flow and there were a few different options on the table. and one of the options that, that people were gravitating to one of the founders didn't like it. And they said a few times that there's this makes no sense, there's no other system in the world that does it this way. And I think one of the other founders said that's exactly why we should do it this way. And, or at least seriously considered this option. So I think there was always this this tendency and this impulse that that we needed to think big and think differently and not see the world the way it is but the, the way we wanted it to be and then work our way backwards and try to make it happen. >> Alison, any fork in the road moments that you remember? >> Well, I'm just thinking back to a really early meeting with Ashish and a few of our founders where we were debating something, probably not super exciting to a lot of people outside of hardcore database people which was how to represent our column metadata. And I think it's funny that you, that you mentioned Yoda because we often make jokes about one of our founders Terry and referred to him as Yoda, because he has this tendency to say very concise things that kind of make you scratch your head and say, "Wow why didn't I think of that?" Or, you know, what exactly does that mean? I never thought about it that way. So I think when I think of the Yoda in the room, it was definitely Terry. >> Ashish, anything you can add to this conversation? >> I'll agree with Alison on the Yoda comment, for sure. Another big fork in the road I recall was when we changed one of our meta store where we store our on internal metadata. We used to use a tool called MySQL and we changed it to another database called FoundationDB, I think that was a big game changer for us. And, you know, it was a tough decision, it took us a long time. For the longest time we even had our own little branch it was called FoundationDB and everybody who was developing on that branch. It's a little embarrassing, but, you know, those are the kinds of decisions that alter the shape of Snowflake. >> Yeah, I mean, these are really, you know, down in the weeds hardcore stuff that a lot of people might not be exposed to. What would you say was the least obvious technical decision that you had to make at the time? And I want to ask you about the most obvious too, but what was the one that was so out of the box? I mean, you kind of maybe mentioned it a little bit before but I wonder if we could double click on that? >> Well, I think one of the core decisions in our architecture is the separation of compute and storage. And, you know, that is really core to our architecture, and there are so many features that we have today for instance, data sharing, zero copy cloning, that we couldn't have without that architecture. And I think it was both not obvious, and when we told people about it in the early days there was definitely skepticism about being able to make that work and being able to have that architecture and still get great performance. >> Exactly. >> Yeah. Anything that was like clearly obvious that maybe that, maybe that was the least and the most that, that separation from compute and store, because it allowed you to actually take advantage of Cloud native. But was there an obvious one that you know, is it sort of dogma that you, you know philosophically live by, you know, to this day? >> I think one really obvious thing is the sort of no tuning, no knobs, ease of use story behind Snowflake. And I say, it's really obvious because everybody wants their system to be easy to use. But then I would say there were tons of decisions behind that, that it's not always obvious, the implications, of such a choice, right? And really sticking to that. And I think that that's really like a core principle behind Snowflake, that led to a lot of non-obvious decisions as a result of sticking to that principle. >> So >> I think, to add to that, now you've grabbed us thinking. I think another really interesting one was really, should we start from scratch or should we use something that already exists and build on top of that? And I think that was one of these almost philosophical kind of stances that we took, that a lot of the systems that were out there were the way they were, because, because they weren't built for the, for the platforms that they were running on. And the big thing that we were targeting was the Cloud. And so one of the big stances we took was that we were going to build from scratch. And we weren't going to borrow a single line of code from many other database out there. And this was something that really shocked a lot of people and many times that this was pretty crazy, and it was, but this is how you build great products. >> That's awesome. All right Ashish, I should give you the last word. We got like just like 30 seconds left, bring us home. >> Till date, actually one of those said shocks people when you talk to them and they say, "Wow, you are naturally using any other database, and you build this entirely yourself." The number of people who actually can build a database from scratch are fairly limited, the group is fairly small. And so it was really a humongous task, and as you've mentioned, you know, it really changed the direction of how we designed a database. What we, what does the database really mean to us, right? The way Snowflake has built a database, it's really a number of organs that come together and form the body. And that's also a concept that's novel to the database industry. >> Guys, congratulations, you must be so proud and it's going to be awesome watching the next decade. So thank you so much for sharing your stories. >> Thanks too. >> Thank you. >> Thank you.

>> Hello everyone, we're here at the Data Cloud Summit, and we have a real treat for you. I call it the CMO Power Panel. And we're going to explore how data is transforming marketing, branding and promotion. And with me are three phenomenal marketing pros and chief marketing officers. Denise Persson is the CMO of Snowflake, Scott Holden of ThoughtSpot and Laura Langdon of Wipro. Folks, great to see you. Thanks so much for coming on "theCUBE." >> Great to be here with you David. >> Awesome, Denise, let's start with you. I want to talk about the role and the changing role of the CMOs, has changed a lot, you know, I suppose of course with all this data, but I wonder what you're experiencing and can you share with us why marketing especially is being impacted by data. >> Well data's really what has helped turn us marketers into revenue drivers, into call centers. And it's clearly a much better place to be. What I'm personally most excited about is the real time access we have to data today. In the past, I used to get a stale report a few weeks after a marketing program was over and at that time we couldn't make any changes to the investments we'd already made. Today, we get data in the midst of running a program. So it can reallocate investments at the time a program is up and running and that's really profound. Today as well, I would say that adaptability has truly become the true superpowers of marketing today and data is really what enables us to adapt to scale. We can adapt to customer's behavior and preferences at scale and that's truly a profound new way of working as well. >> That's interesting what you say cause you know, in tough times used to be okay, sales and engineering, put a brick wall around those and you know, you name it marketing, say, "Okay, cut." But now it's like, you go to marketing and say, "Okay, what's the data say, "how do we have to pivot?" And Scott, I wonder what have data and cloud really brought to the modern marketer that you might not have had before through to this modern era? >> Well, this era, I don't think there's ever been a better time to be a marketer than there is right now. And the primary reason is that we have access to data and insights like we've never had before and I'm not exaggerating when I say that I have a hundred times more access to data than I had a decade ago. It's just phenomenal. When you look at the power of cloud, search, AI, these new consumer experiences for analytics, we can do things in seconds that used to take days. And so it's become in us, as Denise said a super power for us to have access to so much data. And it's, you know, COVID has been hard. A lot of our marketing teams who never worked harder making this pivot from the physical world to the virtual world but they're, you know, at least we're working. And the other part of it is that digital has just created this phenomenal opportunity for us because the beauty of digital and digital transformation is that everything now is trackable, which makes it measurable and means that we can actually get insights that we can act on in a smarter way. And you know, it's worth giving an example. If you just look at this show, right? Like this event that we're viewing. In a physical world, all of you watching at home you'd be in front of us in a room and we'd be able to know if you're in the room, right? We'd track to the scanners when you walked in but that's basically it. At that point, we don't really get a good sense for how much you like, what we're saying. You know, maybe you filled out a survey, but only five to 10% of people ever do that. In a digital world, we know how long you stick around. And as a result, like it's easy, people can just with a click, you know, change the channel. And so the bar for content has gone way up as we do these events but we know how long people are sticking around. And that's, what's so special about it. You know, Denise and her team, as the host of this show they're going to know how long people watch this segment. And that knowing is powerful. I mean, it's simple as you know, using a product like ThoughtSpot, you could just ask a question, you know, how many, you know, what's the average view time by session and Bloomer chart pops up. You're going to know what's working and what's not. And that's something that you can take and act on in the future. And that's what our customers are doing. So, you know, Snowflake and ThoughtSpot, we share our customer with Hulu and they're tracking programs. So, what people are watching at home, how long they're watching, what they're watching next. And they're able to do that in a super granular way and improve their content as a result. And that's the power of this new world we live in that's made the cloud and data so accessible to folks like us. >> Well, thank you for that. And I want to come back to that notion and understand how you're bringing data into your marketing ops, but I want to bring Laura in. Laura, Wipro, you guys partner with a lot of brands, a lot of companies around the world. I mean, thousands of partners, obviously Snowflake in ThoughtSpot or two. How are you using data to optimize these co-marketing relationships? You know, specifically, what what are the trends that you're seeing around things like customer experience? >> So, you know, we use data for all of our marketing decisions, our own, as well as with our partners. And I think what's really been interesting about partner marketing data is we can feed that back to our sales team, right? So, it's very directional for them as well and their efforts moving forward. So, I think that's a place where specifically to partners, it's really powerful. We can also use our collected data to go out to customers to better effect. And then you know, regarding these trends, we just did a survey on the state of the intelligent enterprise. We interviewed 300 companies, US and UK, and there were three interesting I thought statistics relevant to this. Only 22% of the companies that we interviewed felt that their marketing was where it needed to be from an automation standpoint. So lots of room for us to grow, right? Lots of space for us to play. And 61% of them believe that it was critical that they implement this technology to become a more intelligent enterprise. But when they ranked on readiness by function, marketing came in six, right? So HR, RND, finance were all ahead of marketing followed by sales. You know, and then the final data point that I think was interesting was 40% of those agreed that the technology was the most important thing, that thought leadership was critical. You know, and I think that's where marketers really can bring our tried and true experience to bear and merge it with this technology. >> Great, thank you. So, Denise, I've been getting the Kool-Aid injection this week around Data Cloud. I've been pushing people but now that I have the CMO in front of me, I want to ask about the Data Cloud and what it means specifically for the customers and what are some of the learnings maybe that you've experienced that can support some of the things that that Laura and Scott were just discussing. >> Yeah, as Scott said before, idea of a hundred times more data than he ever has before. And that's again, if you look at all the companies we talked to around the world it's not about the amount of data that they have that is the problem, it's the ability to access that data. That data for most companies is trapped across silos, across the organization. It sits in data applications, systems or records. Some of that data sits with your partners that you want to access. And that's really what the data cloud comes in. Data cloud is really mobilizing that data for you. It brings all that data together for you in one place. So you can finally access that data and really provide ubiquitous access to that data to everyone in your organization that needs it and can truly unlock the value of that data. And from a marketing perspective, I mean, we are responsible for the customer experience you know, we provide to our customers and if you have access to all the data on your customers, that's when you have that to customer 360, that we've all been talking about for so many years. And if you have all that data, you can truly, you know, look at their, you know, buying behaviors, put all those dots together and create those exceptional customer experiences. You can do things such as the retailers do in terms of personal decision, for instance, right? And those are the types of experiences, you know, our customers are expecting today. They are expecting a 100% personalized experience for them you know, all the time. And if you don't have all the data, you can't really put those experiences together at scale. And that is really where the data cloud comes in. Again, the data cloud is not only about mobilizing your own data within your enterprise. It's also about having access to data from your partners or extending access to your own data in a secure way to your partners within your ecosystems. >> Yeah, so I'm glad you mentioned a couple of things. I've been writing about this a lot and in particularly the 360 that we were dying for, but haven't really been able to tap. I didn't call it the data cloud, I don't have a marketing gene. I had another sort of boring name for it, but I think there's similar vectors there. So I appreciate that. Scott, I want to come back to this notion of building data DNA in your marketing, you know, fluency and how you put data at the core of your marketing ops. I've been working with a lot of folks in banking and manufacturing and other industries that are that are struggling to do this. How are you doing it? What are some of the challenges that you can share and maybe some advice for your peers out there? >> Yeah, sure, you brought up this concept of data fluency and it's an important one. And there's been a lot of talk in the industry about data literacy and being able to read data. But I think it's more important to be able to speak data, to be fluent and as marketers, we're all storytellers. And when you combine data with storytelling, magic happens. And so, getting a data fluency is a great goal for us to have for all of the people in our companies. And to get to that end, I think one of the things that's happening is that people are hiring wrong and they're thinking about it, they're making some mistakes. And so a couple of things come to mind especially when I look at marketing teams that I'm familiar with. They're hiring a lot of data analysts and data scientists and those folks are amazing and every team needs them. But if you go too big on that, you do yourself a disservice. The second key thing is that you're basically giving your frontline folks, your marketing managers or people on the front lines, an excuse not to get involved with data. And then that's a big mistake because it used to be really hard. But with the technologies available to us now, these new consumer like experiences for data analytics, anybody can do it. And so we as leaders have to encourage them to do it. And I'll give you just a you know, an example, you know, I've got about 32 people on my marketing team and I don't have any data analysts on my team. Across our entire company, we have a couple of analysts and a couple of data engineers. And what's happening is the world is changing where those folks, they're enablers, they architect the system. They bring in the different data sources. They use technologies like Snowflake as being so great at making it easier for people to pull spectrum technology together and to get access to data out of it quickly, but they're pulling it together and then simple things like, "Hey I just want to see this "weekly instead of monthly." You don't need to waste your expensive data science talent. You know, Gardener puts a stat out there that 50% of data scientists are doing basic visualization work. That's not a good use of their time. The products are easy enough now that everyday marketing managers can do that. And when you have a marketing manager come to you and say, you know, "I just figured out "this campaign which looks great on the surface "is doing poorly from an ROI perspective. That's a magic moment. And so we all need to coach our teams to get there. And I would say, you know, lead by example, give them an opportunity to access data and turn it into a story, that's really powerful. And then lastly, praise people who do it, like, use it as something to celebrate inside our companies is a great way to kind of get this initiative. >> I love it. And talking about democratizing data and making it self service, people feel ownership. You know, Laura, Denise was talking about the ecosystem and you're kind of the ecosystem pro here. How does the ecosystem help marketers succeed? Maybe you can talk about the power of many versus the resource of one. >> Sure, you know, I think it's a game changer and it will continue to be. And I think it's really the next level for marketers to harness this power that's out there and use it, you know, and it's something that's important to us, but it's also something we're starting to see our customers demand. You know, we went from a one size fits all solution to they want to bring the best in class to their organization. We all need to be really agile and flexible right now. And I think this ecosystem allows that, you know, you think about the power of Snowflake, Snowflake mining data for you and then a ThoughtSpot really giving you the dashboard to have what you want. And then an implementation partner like a Wipro coming in, and really being able to plug in whatever else you need to deliver. And I think it's really super powerful and I think it gives us you know, it just gives us so much to play with and so much room to grow as marketers. >> Thank you, Denise, why don't you bring us home. We're almost out of time here, but marketing, art, science, both? What are your thoughts? >> Definitely both, I think that's the exciting part about marketing. It is a balancing act between art and science. Clearly, it's probably more science today than it used to be but the art part is really about inspiring change. It's about changing people's behavior and challenging the status quo, right? That's the art part. The science part, that's about making the right decisions all the time, right? It's making sure we are truly investing in what's going to drive revenue for us. >> Guys, thanks so much for coming on "theCUBE." Great discussion, I really appreciate it. Okay, and thank you for watching. Keep it right there. Wall-to-wall coverage of the Snowflake Data Cloud Summit on "theCUBE."

>> Hello everyone, and welcome back to our wall-to-wall coverage of the Datacloud summit, this is Dave Vellante, and we're seeing the emergence of a next generation workload in the cloud, more facile access, and governed sharing of data is accelerating time to insights and action. Alright, allow me to introduce our next guest. Aimee Irwin is here, she's the vice president of strategy for Experian, and Matt Glickman is VP of customer product strategy at Snowflake, with an emphasis on financial services, folks, welcome to theCUBE, thanks so much for coming on. >> Thanks Dave, nice to be here. >> Hey so Aimee, obviously 2020's been pretty unique and crazy and challenging time for a lot of people, I don't know why, I've been checking my credit score a lot more for some reason on the app, I love the app, I had to lock it the other day, I locked my credit, somebody tried to do, and it worked, I was so happy, so thank you for that. So, we know Experian, but there's a ton of data behind what you do, I wonder if you could share kind of where you sit in the data space, and how you've seen organizations leverage data up to this point, and really if you could address some of the changes you're seeing as a result of the pandemic, that would be great. >> Sure, sure. Well, as you mentioned, Experian is best known as a credit bureau. I work in our marketing services business unit, and what we do is we really help brands leverage the power of data and technology to make the right marketing decisions, and better understand and connect with consumers. So we offer marketers products around data, identity, activation, measurement, we have a consumer-view data file that's based on offline PII and contains demographic interest, transaction data, and other attributes on about 300 million people in the US. And on the identity side we've always been known for our safe haven, or privacy-friendly matching, that allows marketers to connect their first party data to Experian or other third parties, but in today's world, with the growth in importance of digital advertising, and consumer behavior shifting to digital, Experian also is working to connect that offline data to the digital world, for a complete view of the customer. You mentioned COVID, we actually, we serve many different verticals, and what we're seeing from our clients during COVID is that there's a varying impact of the pandemic. The common theme is that those who have successfully pivoted their businesses to digital are doing much better, as we all know, COVID accelerated very strong trends to digital, both in e-commerce and in media-viewing habits. We work with a lot of retailers, retail is a tale of two cities, with big box and grocery growing, and apparel retail really struggling. We've helped our clients, leveraging our data to better understand the shifts in these consumer behaviors, and better psych-map their customers during this really challenging time. So think about, there's a group of customers that is still staying home, that is sheltered in place, there's a group of customers starting to significantly vary their consumer behavior, but is starting to venture out a little, and then there's a group of customers that's doing largely what they did before, in a somewhat modified fashion, so we're helping our clients segment those customers into groups to try and understand the right messaging and right offers for each of those groups, and we're also helping them with at-risk audiences. So that's more on the financial side, which of your customers are really struggling due to the pandemic, and how do you respond. >> That's awesome, thank you. You know, it's funny, I saw a twitter poll today asking if we measure our screen time, and I said, "oh my, no." So, Matt, let me ask you, you spent a ton of time in financial services, you really kind of cut your teeth there, and it's always been very data-oriented, you're seeing a lot of changes, tell us about how your customers are bringing it together, data, the skills, the people, obviously a big part of the equation, and applications to really put data at the center of the universe, what's new and different that these companies are getting out of the investments in data and skills? >> That's a great question, the acceleration that Aimee mentioned is real. We're seeing, particularly this year, but I think even in the past few years, the reluctance of customers to embrace the cloud is behind us, and now there's this massive acceleration to be able to go faster, and in some ways, the new entrants into this category have an advantage versus the companies that have been in this space, whether it's financial services or beyond, and in a lot of ways, they all are seeing the cloud and services like Snowflake as a way to not only catch up, but leapfrog your competitors, and really deliver a differentiated experience to your customers, to your business, internally or externally. And this past, however long this crisis has been going on, has really only accelerated that, because now there's a new demand to understand your customer better, your business better, with your traditional data sources, and also new, alternative data sources, and also being able to take a pulse. One of the things that we learned, which was an eye-opening experience, was as the crisis unfolded, one of our data partners decided to take the datasets about where the cases were happening from the Johns Hopkins, and World Health Organization, and put that on our platform, and it became a runaway hit. Thousands of our customers overnight were using this data to understand how their business was doing, versus how the crisis was unfolding in real time. And this has been a game-changer, and it's only scratching the surface of what now the world will be able to do when data is really at their fingertips, and you're not hindered by your legacy platforms. >> I wrote about that back in the early days of the pandemic when you guys did that, and talked about some of the changes that you guys enabled, and you know, you're right about cloud, in financial services cloud used to be an evil word, and now it's almost, it's become a mandate. Aimee, I wonder if you could tell us a little bit more about what your customers are having to work through in order to achieve some of these outcomes. I mean, you know, I'm interested in the starting point, I've been talking a lot, and writing a lot, and talking to practitioners about what I call the data life cycle, sometimes people call it the data pipeline, it's a complicated matter, but those customers and companies that can put data at the center and really treat that pipeline as the heart of their organization, if you will, are really succeeding. What are you seeing, and what really is the starting point, there? >> Yes, yeah, that's a good question, and as you mentioned, first party, I mean we start with first party data, right? First party data is critical to understanding consumers. And different verticals, different companies, different brands have varying levels of first party data. So a retailers going to have a lot more first party data, a financial services company, than say, an auto manufacturer. And while many marketers have that first party data, to really have a 360 view of the customer, they need third party data as well, and that's where Experian comes in, we help brands connect those disparate datasets, both first and third party data to better understand consumers, and create a single customer view, which has a number of applications. I think the last stat I heard was that there's about eight devices, on average, per person. I always joke that we're going to have these enormous, and that number's growing, we're going to have these enormous charging stations in our house, and I think we already do, because of all the different devices. And we seamlessly move from device to device, along our customer journey, and, if the brand doesn't understand who we are, it's much harder for the brand to connect with consumers and create a positive customer experience. And we cite that about 95 percent of companies, they are looking to achieve that single customer view, they recognize that they need that, and they've aligned various teams from e-commerce, to marketing, to sales, to at a minimum adjust their first party data, and then connect that data to better understand consumers. So, consumers can interact with a brand through a website, a mobile app, in-store visits, you know, by the phone, TV ads, et cetera, and a brand needs to use all of those touchpoints, often collected by different parts of the organization, and then add in that third party data to really understand the consumers. In terms of specific use cases, there's about three that come to mind. So first there's relevant advertising, and reaching the right customer, there's measurement, so being able to evaluate your advertising efforts, if you see an ad on, if I see an ad on my mobile, and then I buy by visiting a desktop website, understanding, or I get a direct mail piece, understanding that those interactions are all connected to the same person is critical for measurement. And then there's personalization, which includes improved customer experience amongst your own touchpoints with that consumer, personalized marketing communication, and then of course analytics, so those are the use cases we're seeing. >> Great, thank you Aimee. Now Matt, you can't really talk about data without talking about governance and compliance, and I remember back in 2006, when the federal rules of civil procedure went in, it was easy, the lawyers just said, "no, nobody can have access," but that's changed, and one of the things I like about what Snowflake's doing with the data cloud is it's really about democratizing access, but doing so in a way that gives people confidence that they only have access to the right data. So maybe you could talk a little bit about how you're thinking about this topic, what you're doing to help customers navigate, which has traditionally been such a really challenging problem. >> Another great question, this is where I think the major disruption is happening. And what Aimee described, being able to join together first and third party datasets, being able to do this was always a challenge, because data had to be moved around, I had to ship my first party data to the other side, and the third party data had to be shipped to me, and being able to join those datasets together was problematic at best, and now with the focus on privacy and protecting PII, this is something that has to change, and the good news is, with the data cloud, data does not have to move. Data can stay where it belongs, Experian can keep its data, Experian's customers can hold onto their data, yet the data can be joined together on this universal, global platform that we call the data cloud. On top of that, and particularly with the regulations that are coming out that are going to prevent data from being collected on either a mobile device or as cookies on web browsers, new approaches, and we're seeing this a lot in our space, both in financials and media, is to set up these data clean rooms, where both sides can give access to one another, but not have to reveal any PII to do that join. This is going to be huge, now you actually can protect your customers' and your consumers' private identities, but still accomplish that join that Aimee mentioned, to be able to relate the cause and effect of these campaigns, and really understand the signals that these datasets are trying to say about one another, again without having to move data, without having to reveal PII, we're seeing this happening now, this is the next big thing, that we're going to see explode over the months and years to come. >> I totally agree, massive changes coming in public policy in this area, and we only have a few minutes left, and I wonder if for our audience members that are looking for some advice, what's the, Aimee, what's the one thing you'd recommend they start doing differently, or consider putting in place that's going to set them up for success over the next decade? >> Yeah, that's a good question. You know, I think, I always say, first, harness all of your first party data across all touchpoints, get that first party data in one place and working together, second, connect that data with trusted third parties, and Matt suggested some ways to do that, and then third, always put the customer first, speak their language, where and when they want to be reached out to, and use the information you have to really create a better customer experience for your customers. >> Matt, what would you add to that? Bring us home, if you would. >> Applications. The idea that data, your data can now be pulled into your own business applications the same way that Netflix and Spotify are pulled into your consumer and lifestyle applications, again, without data moving, these personalized application experiences is what I encourage everyone to be thinking about from first principles. What would you do in your next app that you're going to build, if you had all your consumers, if the consumers had access to their data in the app, and not having to think about things from scratch, leverage the data cloud, leverage these service providers like Experian, and build the applications of tomorrow. >> I'm super excited when I talk to practitioners like yourselves, about the future of data, guys, thanks so much for coming on theCUBE, it was a really a pleasure having you, and I hope we can continue this conversation in the future. >> Thank you. >> Thanks. >> Alright, thank you for watching, keep it right there, we got great content, and tons of content coming at the Snowflake data cloud summit, this is Dave Vellante for theCUBE, keep it right there.

>> My name is Dave Vellante. And with me are two world-class technologists, visionaries and entrepreneurs. Benoit Dageville, he co-founded Snowflake and he's now the President of the Product Division, and Florian Douetteau is the Co-founder and CEO of Dataiku. Gentlemen, welcome to the cube to first timers, love it. >> Yup, great to be here. >> Now Florian you and Benoit, you have a number of customers in common, and I've said many times on theCUBE, that the first era of cloud was really about infrastructure, making it more agile, taking out costs. And the next generation of innovation, is really coming from the application of machine intelligence to data with the cloud, is really the scale platform. So is that premise relevant to you, do you buy that? And why do you think Snowflake, and Dataiku make a good match for customers? >> I think that because it's our values that aligned, when it gets all about actually today, and knowing complexity of our customers, so you close the gap. Where we need to commoditize the access to data, the access to technology, it's not only about data. Data is important, but it's also about the impacts of data. How can you make the best out of data as fast as possible, as easily as possible, within an organization. And another value is about just the openness of the platform, building a future together. Having a platform that is not just about the platform, but also for the ecosystem of partners around it, bringing the level of accessibility, and flexibility you need for the 10 years of that. >> Yeah, so that's key, that it's not just data. It's turning data into insights. Now Benoit, you came out of the world of very powerful, but highly complex databases. And we know we all know that you and the Snowflake team, you get very high marks for really radically simplifying customers' lives. But can you talk specifically about the types of challenges that your customers are using Snowflake to solve? >> Yeah, so the challenge before snowflake, I would say, was really to put all the data in one place, and run all the computes, all the workloads that you wanted to run against that data. And of course existing legacy platforms were not able to support that level of concurrency, many workload, we talk about machine learning, data science, data engineering, data warehouse, big data workloads, all running in one place didn't make sense at all. And therefore be what customers did this to create silos, silos of data everywhere, with different system, having a subset of the data. And of course now, you cannot analyze this data in one place. So Snowflake, we really solved that problem by creating a single architecture where you can put all the data into cloud. So it's a really cloud native. We really thought about how solve that problem, how to create, leverage cloud, and the elasticity of cloud to really put all the data in one place. But at the same time, not run all workload at the same place. So each workload that runs in Snowflake, at its dedicated compute resources to run. And that makes it agile, right? Florian talked about data scientist having to run analysis, so they need a lot of compute resources, but only for a few hours. And with Snowflake, they can run these new workload, add this workload to the system, get the compute resources that they need to run this workload. And then when it's over, they can shut down their system, it will automatically shut down. Therefore they would not pay for the resources that they don't use. So it's a very agile system, where you can do this analysis when you need, and you have all the power to run all these workload at the same time. >> Well, it's profound what you guys built. I mean to me, I mean of course everybody's trying to copy it now, it was like, I remember that bringing the notion of bringing compute to the data, in the Hadoop days. And I think that, as I say, everybody is sort of following your suit now or trying to. Florian, I got to say the first data scientist I ever interviewed on theCUBE, it was the amazing Hillary Mason, right after she started at Bitly, and she made data sciences sounds so compelling, but data science is a hard. So same question for you, what do you see as the biggest challenges for customers that they're facing with data science? >> The biggest challenge from my perspective, is that once you solve the issue of the data silo, with Snowflake, you don't want to bring another silo, which will be a silo of skills. And essentially, thanks to the talent gap, between the talent available to the markets, or are released to actually find recruits, train data scientists, and what needs to be done. And so you need actually to simplify the access to technologies such as, every organization can make it, whatever the talent, by bridging that gap. And to get there, there's a need of actually backing up the silos. Having a collaborative approach, where technologies and business work together, and actually all puts up their ends into those data projects together. >> It makes sense, Florain let's stay with you for a minute, if I can. Your observation space, it's pretty, pretty global. And so you have a unique perspective on how can companies around the world might be using data, and data science. Are you seeing any trends, maybe differences between regions, or maybe within different industries? What are you seeing? >> Yeah, definitely I do see trends that are not geographic, that much, but much more in terms of maturity of certain industries and certain sectors. Which are, that certain industries invested a lot, in terms of data, data access, ability to store data. As well as experience, and know region level of maturity, where they can invest more, and get to the next steps. And it's really relying on the ability of certain leaders, certain organizations, actually, to have built these long-term data strategy, a few years ago when no stats reaping of the benefits. >> A decade ago, Florian, Hal Varian famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of changed that to data scientist. And then everybody, all the statisticians became data scientists, and they got a raise. But data science requires more than just statistics acumen. What skills do you see as critical for the next generation of data science? >> Yeah, it's a great question because I think the first generation of data scientists, became data scientists because they could have done some Python quickly, and be flexible. And I think that the skills of the next generation of data scientists will definitely be different. It will be, first of all, being able to speak the language of the business, meaning how you translates data insight, predictive modeling, all of this into actionable insights of business impact. And it would be about how you collaborate with the rest of the business. It's not just how fast you can build something, how fast you can do a notebook in Python, or do predictive models of some sorts. It's about how you actually build this bridge with the business, and obviously those things are important, but we also must be cognizant of the fact that technology will evolve in the future. There will be new tools, new technologies, and they will still need to keep this level of flexibility to understand quickly what are the next tools they need to use a new languages, or whatever to get there. >> As you look back on 2020, what are you thinking? What are you telling people as we head into next year? >> Yeah, I think it's very interesting, right? This crises has told us that the world really can change from one day to the next. And this has dramatic and perform the aspects. For example companies all of a sudden, show their revenue line dropping, and they had to do less with data. And some other companies was the reverse, right? All of a sudden, they were online like Instacart, for example, and their business completely changed from one day to the other. So this agility of adjusting the resources that you have to do the task, and need that can change, using solution like Snowflake really helps that. Then we saw both in our customers. Some customers from one day to the next, were growing like big time, because they benefited from COVID, and their business benefited. But others had to drop. And what is nice with cloud, it allows you to adjust compute resources to your business needs, and really address it in house. The other aspect is understanding what happening, right? You need to analyze. We saw all our customers basically, wanted to understand what is the going to be the impact on my business? How can I adapt? How can I adjust? And for that, they needed to analyze data. And of course, a lot of data which are not necessarily data about their business, but also they are from the outside. For example, COVID data, where is the States, what is the impact, geographic impact on COVID, the time. And access to this data is critical. So this is the premise of the data cloud, right? Having one single place, where you can put all the data of the world. So our customer obviously then, started to consume the COVID data from that our data marketplace. And we had delete already thousand customers looking at this data, analyzing these data, and to make good decisions. So this agility and this, adapting from one hour to the next is really critical. And that goes with data, with cloud, with interesting resources, and that doesn't exist on premise. So indeed I think the lesson learned is we are living in a world, which is changing all the time, and we have to understand it. We have to adjust, and that's why cloud some ways is great. >> Excellent thank you. In theCUBE we like to talk about disruption, of course, who doesn't? And also, I mean, you look at AI, and the impact that it's beginning to have, and kind of pre-COVID. You look at some of the industries that were getting disrupted by, everyone talks about digital transformation. And you had on the one end of the spectrum, industries like publishing, which are highly disrupted, or taxis. And you can say, okay, well that's Bits versus Adam, the old Negroponte thing. But then the flip side of, you say look at financial services that hadn't been dramatically disrupted, certainly healthcare, which is ripe for disruption, defense. So there a number of industries that really hadn't leaned into digital transformation, if it ain't broke, don't fix it. Not on my watch. There was this complacency. And then of course COVID broke everything. So Florian I wonder if you could comment, what industry or industries do you think are going to be most impacted by data science, and what I call machine intelligence, or AI, in the coming years and decade? >> Honestly, I think it's all of them, or at least most of them, because for some industries, the impact is very visible, because we have talking about brand new products, drones, flying cars, or whatever that are very visible for us. But for others, we are talking about a part from changes in the way you operate as an organization. Even if financial industry itself doesn't seem to be so impacted, when you look at it from the consumer side, or the outside insights in Germany, it's probably impacted just because the way you use data (mumbles) for flexibility you need. Is there kind of the cost gain you can get by leveraging the latest technologies, is just the numbers. And so it's will actually comes from the industry that also. And overall, I think that 2020, is a year where, from the perspective of AI and analytics, we understood this idea of maturity and resilience, maturity meaning that when you've got to crisis you actually need data and AI more than before, you need to actually call the people from data in the room to take better decisions, and look for one and a backlog. And I think that's a very important learning from 2020, that will tell things about 2021. And the resilience, it's like, data analytics today is a function transforming every industries, and is so important that it's something that needs to work. So the infrastructure needs to work, the infrastructure needs to be super resilient, so probably not on prem or not fully on prem, at some point. And the kind of resilience where you need to be able to blend for literally anything, like no hypothesis in terms of BLOs, can be taken for granted. And that's something that is new, and which is just signaling that we are just getting to a next step for data analytics. >> I wonder Benoir if you have anything to add to that. I mean, I often wonder, when are machines going to be able to make better diagnoses than doctors, some people say already. Will the financial services, traditional banks lose control of payment systems? What's going to happen to big retail stores? I mean, maybe bring us home with maybe some of your finals thoughts. >> Yeah, I would say I don't see that as a negative, right? The human being will always be involved very closely, but then the machine, and the data can really help, see correlation in the data that would be impossible for human being alone to discover. So I think it's going to be a compliment not a replacement. And everything that has made us faster, doesn't mean that we have less work to do. It means that we can do more. And we have so much to do, that I will not be worried about the effect of being more efficient, and bare at our work. And indeed, I fundamentally think that data, processing of images, and doing AI on these images, and discovering patterns, and potentially flagging disease way earlier than it was possible. It is going to have a huge impact in health care. And as Florian was saying, every industry is going to be impacted by that technology. So, yeah, I'm very optimistic. >> Great, guys, I wish we had more time. I've got to leave it there, but so thanks so much for coming on theCUBE. It was really a pleasure having you.

>> Hi and welcome to Data Cloud catalyst Women in Tech Round Table Panel discussion. I am so excited to have three fantastic female executives with me today, who have been driving transformations through data throughout their entire career. With me today is Lisa Davis, SVP and CIO OF Blue shield of California. We also have Nishita Henry who is the Chief Innovation Officer at Deloitte and Teresa Briggs who is on a variety of board of directors including our very own Snowflake. Welcome ladies. >> Thank you. >> So I am just going to dive right in, you all have really amazing careers and resumes behind you, am really curious throughout your career, how have you seen the use of data evolve throughout your career and Lisa am going to start with you. >> Thank you, having been in technology my entire career, technology and data has really evolved from being the province of a few in an organization to frankly being critical to everyone's business outcomes. Now every business leader really needs to embrace data analytics and technology. We've been talking about digital transformation, probably the last five, seven years, we've all talked about, disrupt or be disrupted, At the core of that digital transformation is the use of data. Data and analytics that we derive insights from and actually improve our decision making by driving a differentiated experience and capability into market. So data has involved as being I would say almost tactical, in some sense over my technology career to really being a strategic asset of what we leverage personally in our own careers, but also what we must leverage as companies to drive a differentiated capability to experience and remain relative in the market today. >> Nishita curious your take on, how you have seen data evolve? >> Yeah, I agree with Lisa, it has definitely become a the lifeblood of every business, right? It used to be that there were a few companies in the business of technology, every business is now a technology business. Every business is a data business, it is the way that they go to market, shape the market and serve their clients. Whether you're in construction, whether you're in retail, whether you're in healthcare doesn't matter, right? Data is necessary for every business to survive and thrive. And I remember at the beginning of my career, data was always important, but it was about storing data, it was about giving people individual reports, it was about supplying that data to one person or one business unit in silos. And it then evolved right over the course of time into integrating data into saying, alright, how does one piece of data correlate to the other and how can I get insights out of that data? Now, its gone to the point of how do I use that data to predict the future? How do I use that data to automate the future? How do I use that data not just for humans to make decisions, but for other machines to make decisions, right? Which is a big leap and a big change in how we use data, how we analyze data and how we use it for insights and involving our businesses. >> Yeah its really changed so tremendously just in the past five years, its amazing. So Teresa we've talked a lot about the Data Cloud, where do you think we are heading with that and also how can future leaders really guide their careers in data especially in those jobs where we don't traditionally think of them in the data science space? Teresa your thoughts on that. >> Yeah, well since I'm on the Snowflake Board, I'll talk a little bit about the Snowflake Data Cloud, we're getting your company's data out of the silos that exist all over your organization. We're bringing third party data in to combine with your own data and we're wrapping a governance structure around it and feeding it out to your employees so they can get their jobs done, as simple as that. I think we've all seen the pandemic accelerate the digitization of our work. And if you ever doubted that the future of work is here, it is here and companies are scrambling to catch up by providing the right amount of data, collaboration tools, workflow tools for their workers to get their jobs done. Now, it used to be as prior people have mentioned that in order to work with data you had to be a data scientist, but I was an auditor back in the day we used to work on 16 column spreadsheets. And now if you're an accounting major coming out of college joining an auditing firm, you have to be tech and data savvy because you're going to be extracting, manipulating, analyzing and auditing data, that massive amounts of data that sit in your clients IT systems. I'm on the board of Warby Parker, and you might think that their most valuable asset is their amazing frame collection, but it's actually their data, their 360 degree view of the customer. And so if you're a merchant, or you're in strategy, or marketing or talent or the Co-CEO, you're using data every day in your work. And so I think it's going to become a ubiquitous skill that any anyone who's a knowledge worker has to be able to work with data. >> Yeah I think its just going to be organic to every role going forward in the industry. So, Lisa curious about your thoughts about Data Cloud, the future of it and how people can really leverage it in their jobs for future leaders. >> Yeah, absolutely most enterprises today are, I would say, hybrid multicloud enterprises. What does that mean? That means that we have data sitting on-prem, we have data sitting in public clouds through software as a service applications. We have a data everywhere. Most enterprises have data everywhere, certainly those that have owned infrastructure or weren't born on the web. One of the areas that I love that Data Cloud is addressing is area around data portability and mobility. Because I have data sitting in various locations through my enterprise, how do I aggregate that data to really drive meaningful insights out of that data to drive better business outcomes? And at Blue Shield of California, one of our key initiatives is what we call an Experienced Cube. What does that mean? That means how do I drive transparency of data between providers, members and payers? So that not only do I reduce overhead on providers and provide them a better experience, our hospital systems are doctors, but ultimately, how do we have the member have it their power of their fingertips the value of their data holistically, so that we're making better decisions about their health care. One of the things Teresa was talking about, was the use of this data and I would drive to data democratization. We got to put the power of data into the hands of everyone, not just data scientists, yes we need those data scientists to help us build AI models to really drive and tackle these tough old, tougher challenges and business problems that we may have in our environments. But everybody in the company both on the IT side, both on the business side, really need to understand of how do we become a data insights driven enterprise, put the power of the data into everyone's hands so that we can accelerate capabilities, right? And leverage that data to ultimately drive better business results. So as a leader, as a technology leader, part of our responsibility, our leadership is to help our companies do that. And that's really one of the exciting things that I'm doing in my role now at Blue Shield of California. >> Yeah its really, really exciting time. I want to shift gears a little bit and focus on women in Tech. So I think in the past five to ten years there has been a lot of headway in this space but the truth is women are still under represented in the tech space. So what can we do to attract more women into technology quite honestly. So Nishita curious what your thoughts are on that? >> Great question and I am so passionate about this for a lot of reasons, not the least of which is I have two daughters of my own and I know how important it is for women and young girls to actually start early in their love for technology and data and all things digital, right? So I think it's one very important to start early started early education, building confidence of young girls that they can do this, showing them role models. We at Deloitte just partnered with LV Engineer to actually make comic books centered around young girls and boys in the early elementary age to talk about how heroes in tech solve everyday problems. And so really helping to get people's minds around tech is not just in the back office coding on a computer, tech is about solving problems together that help us as citizens, as customers, right? And as humanity, so I think that's important. I also think we have to expand that definition of tech, as we just said it's not just about right, database design, It's not just about Java and Python coding, it's about design, it's about the human machine interfaces, it's about how do you use it to solve real problems and getting people to think in that kind of mindset makes it more attractive and exciting. And lastly, I'd say look we have a absolute imperative to get a diverse population of people, not just women, but minorities, those with other types of backgrounds, disabilities, et cetera involved because this data is being used to drive decision making in all involved, right, and how that data makes decisions, it can lead to unnatural biases that no one intended but can happen just 'cause we haven't involved a diverse enough group of people around it. >> Absolutely, lisa curious about your thoughts on this. >> I agree with everything Nishita said, I've been passionate about this area, I think it starts with first we need more role models, we need more role models as women in these leadership roles throughout various sectors. And it really is it starts with us and helping to pull other women forward. So I think certainly it's part of my responsibility, I think all of us as female executives that if you have a seat at the table to leverage that seat at the table to drive change, to bring more women forward more diversity forward into the boardroom and into our executive suites. I also want to touch on a point Nishita made about women we're the largest consumer group in the company yet we're consumers but we're not builders. This is why it's so important that we start changing that perception of what tech is and I agree that it starts with our young girls, we know the data shows that we lose our like young girls by middle school, very heavy peer pressure, it's not so cool to be smart, or do robotics, or be good at math and science, we start losing our girls in middle school. So they're not prepared when they go to high school, and they're not taking those classes in order to major in these STEM fields in college. So we have to start the pipeline early with our girls. And then I also think it's a measure of what your boards are doing, what is the executive leadership in your goals around diversity and inclusion? How do we invite more diverse population to the decision making table? So it's really a combination of efforts. One of the things that certainly is concerning to me is during this pandemic, I think we're losing one in four women in the workforce now because of all the demands that our families are having to navigate through this pandemic. The last statistic I saw in the last four months is we've lost 850,000 women in the workforce. This pipeline is critical to making that change in these leadership positions. >> Yeah its really a critical time and now we are coming to the end of this conversation I want to ask you Teresa what would be a call to action to everyone listening both men and women since its to be solved by everyone to address the gender gap in the industry? >> I'd encourage each of you to become an active sponsor. Research shows that women and minorities are less likely to be sponsored than white men. Sponsorship is a much more active form than mentorship. Sponsorship involves helping someone identify career opportunities and actively advocating for them and those roles opening your network, giving very candid feedback. And we need men to participate too, there are not enough women in tech to pull forward and sponsor the high potential women that are in our pipelines. And so we need you to be part of the solution. >> Nishita real quickly what would be your call to action to everyone? >> I'd say look around your teams, see who's on them and make deliberate decisions about diversifying those teams, as positions open up, make sure that you have a diverse set of candidates, make sure that there are women that are part to that team and make sure that you are actually hiring and putting people into positions based on potential not just experience. >> And real quickly Lisa, we'll close it out with you what would your call to action be? >> Wow, it's hard to what Nishita and what Tricia shared I think we're very powerful actions. I think it starts with us. Taking action at our own table, making sure you're driving diverse panels and hiring setting goals for the company, having your board engaged and holding us accountable and driving to those goals will help us all see a better outcome with more women at the executive table and diverse populations. >> Great advice and great action for all of us to take. Thank you all so much for spending time with me today and talking about this really important issue, I really appreciate it. Stay with us.

(upbeat music) >> From the CUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hi everyone, this is Dave Vellante from the CUBE. And we're getting ready for the Snowflake Data cloud summit four geographies, eight tracks more than 40 sessions for this global event. Starts on November 17th, where we're tracking the rise of the Data cloud. You're going to hear a lot about that, now, by now, you know, the story of Snowflake or you know, what maybe you don't but a new type of cloud native database was introduced in the middle part of last decade. And a new set of analytics workloads has emerged that is powering a transformation within organizations. And it's doing this by putting data at the core of businesses and organizations. You know, for years we marched to the cadence of Moore's law. That was the innovation engine of our industry, but now that's changed it's data plus machine intelligence plus cloud. That's the new innovation cocktail for the technology industry and industries overall. And at the Data cloud summit we'll hear from Snowflake executives, founders, technologists, customers, and ecosystems partners. And of course, you going to hear from interviews on the CUBE. So, let's dig in a little bit more and help me are two Snowflake experts. Felipe Hoffa is a data cloud advocate and Kent Graziano is a chief technical evangelist post at Snowflake. Gents, great to see you. Thanks for coming on. >> Yeah, thanks for having us on, this is great. >> Thank you. >> So guys first, I got to congratulate you on getting to this point. You've achieved beyond escape velocity and obviously one of the most important IPOs of the year, but you got a lot of work to do. I know that what, what are the substantive aspects behind the Data cloud? >> I mean, it's a new concept right? We've been talking about infrastructure clouds and SaaS applications living in application clouds and Data cloud is the ability to really share all that data that we've been collected. You know, we've spent what how many a decade or more with big data now but have we been able to use it effectively? And that's really where the Data cloud is coming in and Snowflake and making that a more seamless, friendly, easy experience to get access to the data. I've been in data warehousing for nearly 30 years now. And our dream has always been to be able to augment an organization's analytics with data from outside their organization. And that's just been a massive pain in the neck with having to move files around and replicate the data and maybe losing track of where it came from or where it went. And the Data cloud is really giving our customers the ability to do that in a much more governed way, a much more seamless way and really make it push button to give anyone access to the data they need and have the performance to do the analytics in near real time. It's total game changer is as you already know and just it's crazy what we're able to do today compared it to what we could do when I started out in my career. >> Well, I'm going to come back to that 'cause I want to tap your historical perspective, but Felipe let me ask you, So, why did you join Snowflake? You're you're the newbie here? What attracted you? >> Exactly? I'm the newbie, I used to work at Google until August. I was there for 10 years. I was a developer advocate there also for data you might have heard about the BigQuery. I was doing a lot of that. And then as time went by Snowflake started showing up more and more in my feeds within my customers in my community. And it came the time, well, I felt that like, you know, when wherever you're working, once in a while you think I should leave this place I should try something new, I should move my career forward. While at Google, I thought that so many times, as anyone would do, and it was only when Snowflake showed up, like where Snowflake is going now, why Snowflake is being received by all the customers that I saw this opportunity. And I decided that moving to Snowflake would be a step forward for me. And so far I'm pretty happy, like the timing has been incredible, but more than the timing and everything, it's really, really a great place for data. What I love first is data, sharing data, analyzing data and how Snowflake is doing it's for me to mean phenomenal. >> So, Kent, I want to come back to you and I say tap maybe your historical perspective here. And you said it's always been a dream that you could do these other things bringing in external data. I would say this, that I don't want to push a little bit on this because I have often said that the EDW marketplace really never lived up to its promises of 360 degree views of the customer real time or near real time analytics. And, and it really has been as you kind of described are a real challenge for a lot of organizations. When Hadoop came in we got excited that it was going to actually finally live up to that vision and, and duped it a lot and don't get me wrong, I mean, the whole concept of bring that compute to data and lowering the cost and so forth. But it certainly didn't minimize complexity. And, and it seems like, feels like Snowflake is on the cusp of actually delivering on that promise that we've been talking about for 30 years. I wonder, if you could share your perspective is it, are we going to get there this time? >> Yeah. And as far as I can tell working with all of our customers some of them are there. I mean, they thought through those struggles that you were talking about that I saw throughout my career and now with getting on Snowflake they're delivering customer 360 they're integrating weblogs and IOT data with structured data from their ERP systems or CRM systems, their supply chain systems. And it really is coming to fruition. I mean, the industry leaders, you know, Bill Inman and Claudia Imhoff, they've had this vision the whole time but the technology just wasn't able to support it. And the cloud, as we said about the internet, changed everything. And then Ben wine teary, and they're in their vision and building the system, taking the best concepts from the Hadoop world and the data Lake world and the enterprise data warehouse world and putting it all together into this, this architecture that's now Snowflake and the Data cloud solve it. I mean, it's the classic benefit of hindsight is 2020 after years in the industry, they'd seen these problems and said like, how can we solve them? Does the Cloud let us solve these problems? And the answer was yes, but it did require writing everything from scratch and starting over with, because the architecture of the Cloud just allows you to do things that you just couldn't do before. >> Yeah. I'm glad you brought up you know, some of the originators of the data warehouse because it really wasn't their fault. They were trying to solve a problem. It was the marketers that took it and really kind of made promises that they couldn't keep. But, the reality is when you talk to customers in the so old EDW days and this is the other thing I want to tap you guys' brains on. It was very challenging. I mean, one customer one time referred to it as a snake, swallowing a basketball. And what he meant by that is every time there's a change Sarbanes Oxley comes and we have to ingest all this new data. It's like, Oh, it's to say everything slows down to a grinding halt. Every time Intel came out with a new microprocessor, they would go out and grab a new server as fast as they possibly could. He called it chasing the chips and it was this endless cycle of pain. And so, you know, the originators of the data whereas they didn't have the compute power they didn't have the Cloud. And so, and of course they didn't have the 30, 40 years of pain to draw upon. But I wonder if you could, could maybe talk a little bit about the kinds of things that can be done now that we haven't been able to do here to form. >> Well, yeah. I remember early on having a conversation with Bill about this idea of near real time data warehousing and saying, is this real, is this something really people need? And at the time he was a couple of decades ago, he said now to them they just want to load their data sooner than once a month. That was the goal. And that was going to be near real time for them. And, but now I'm seeing it with our customers. It's like, now we can do it, you know, with things like the Kafka technology and snow pipe in Snowflake that people are able to get that refresh way faster and have near real time analytics access to that data in a much more timely manner. And so it really is coming true. And the, the compute power that's there, as you said, we've now got this compute power in the Cloud that we never dreamed of. I mean, you would think of only certain, very large, massive global companies or governments could afford super computers. And that's what it would have taken. And now we've got nearly the power of a super computer in our mobile device that we all carry around with us. So being able to harness all that now in the Cloud is really opening up opportunities to do things with data and access data in a way that, again really, we just kind of dreamed of before as like we can democratize data when we get to this point. And I think that's where we are. We're at that inflection point where now it's possible to do it. So the challenge on organizations is going to be how do we do it effectively? How do we do it with agility? And how do we do it in a governed manner? You mentioned Sarbanes Oxley, GDPR, CCPA, all of those are out there. And so we have all of that as well. And so that's where we're going to get into it, right into the governance and being able to do that in a very quick, flexible, extensible manner and Snowflakes really letting people do it now. >> Well, yeah. And you know, again, we've been talking about Hadoop and I, again, for all my fond thoughts of that era, and it's not like Hadoop is gone but it was a lot of excitement around it, but governance was a huge problem. And it was kind of a bolt on. Now, Felipe I going to ask you, like, when you think about a company like Google, your former employer, you know, data is at the core of their business. And so many companies the data is not at the core of their business. Something else is, it's a process or a manufacturing facility or whatever it is. And the data is sort of on the outskirts. You know, we often talk about in, in stove pipes. And so we're now seeing organizations really put data at the core of their, it becomes central to their DNA. I'm curious as to your thoughts on that. And also, if you've got a lot of experience with developers, is there a developer angle here in this new data world? >> For sure, I mean, I love seeing everything like throughout my career at Google and my two months here and talking to so many companies, you never thought before like these are database companies but they are the ones that keep rowing. The ones that keep moving to the next stage of their development is because they are focusing on data. They are adapting the processes, they are learning from it. Me, I focus a lot on developers. So, I met when I started this career as an advocate of first, I was a software engineer and my work so far, has we worked, I really loved talking to the engineers on the other companies. Like, maybe I'm not the one solving the business problem, but at the end of the day, when these companies have a business problem that they want to grow, they want to have data. There are other engineers that are scientists like me that want to work for the company and bring the best technology to solve the problems. And Yeah, there's so much where data can help, yes, as we evolved the system for the company, and also for us, for understanding the systems things like of survivability, and recently there was a big company a big launch on survivability (indistinct) whether they are running all of their data warehousing needs. And all of that needs on snowflake, just because running these massive systems and being able to see how they're working generates a lot of data. And then how do you manage it? How do you analyze it? Or Snowflake is really there to help cover the two areas. >> It's interesting my business partner, John farrier cohost of the CUBE, he said, gosh I would say middle of the last decade, maybe even around the time 2013, when Snowflake was just coming out, he said, he predicted the data would be the new development kit. And it's really at the center of a lot of the data life cycle the what I call the data pipelines. I know people use that term differently but I'm very excited about the Data cloud summit and what we're going to learn there. And I get to interview a lot of really cool people. So, I appreciate you guys coming up, but, Kent who should attend the Data cloud summit, I mean, what should they expect to learn? >> Well, as you said earlier, Dave, there's so many tracks and there's really kind of something for everyone. So, we've got a track on unlocking the value of the Data cloud, which is really going to speak to the business leaders, you know, as to what that vision is, what can we do from an organizational perspective with the Data cloud to get that value from the data to move our businesses forward. But we've also done for the technicians migrating to snowflake. Sessions on how to do the migration, modernizing your data Lake, data science, how to do analytics with the, and data science in Snowflake and in the Data cloud, and even down to building apps. So the developers and building data products. So, you know, we've got stuff for developers, we've got stuff for data scientists. We've got stuff for the data architects like myself and the data engineers on how to build all of this out. And then there's going to be some industry solution spotlights as well. So we can talk about different verticals folks in FinTech and healthcare, there's going to be stuff for them. And then for our data superheroes we have a hallway track where we're going to get talks from the folks that are in our data superheroes which is really our community advocacy program. So these are folks who are out there in the trenches using Snowflake delivering value at their organizations. And they're going to talk down and dirty. How did they make this stuff happen? So it's going to be to some hope, really something for everyone, fireside chats with our executives. Of course something I'm really looking forward to myself. So was fun to hear from Frank and Christian and Benoit about what's the next big thing, what are we doing now? Where are we going with all of this? And then there is going to be a some awards we'll be giving out our data driver awards for our most innovative customers. So this is going to be a lot, a lot for everybody to consume and enjoy and learn about this, this new space of, of the Data cloud. >> Well, thank you for that Kent. And I'll second that, at least there's going to be a lot for everybody. If you're an existing Snowflake customer there's going to be plenty of two or one content, we can get in to the how to use and the best practice, if you're really not that familiar with Snowflake, or you're not a customer, there's a lot of one-on-one content going on. So, Felipe, I'd love to hear from you what people can expect at the Data cloud summit. >> Totally, so I would like to plus one to everyone that can say we have a phenomenal schedule that they, the executive will be there. I really wanted to especially highlight the session I'm preparing with Trevor Noah. I'm sure you might have heard of him. And we are having him at the Data cloud summit and we are going to have a session. We are going to talk about data. We are preparing a session. That's all about how people that love data that people that want to make that actionable. How can they bring storytelling and make it more, have more impact as he has well learn to do through his life? >> That's awesome, So, we have Trevor Noah, we're not just going to totally geek out here. we're going to have some great entertainment as well. So, I want you to go to snowflake.com and click on Data cloud summit 2020 there's four geos. It starts on November 17th and then runs through the week and in the following week in Japan. So, so check that out. We'll see you there. This is Dave Vellante for the CUBE. Thanks for watching. (upbeat music)

>> (Instructor)From the cube studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a cube conversation. >> Hi everyone. This is Dave Volante, the cube, and we're getting ready for the snowflake data cloud summit four geographies eight tracks, more than 40 sessions for this global event starts on November 17th, where we're tracking the rise of the data cloud. You're going to hear a lot about that now by now, you know the story of Snowflake or you know, what maybe you don't, but a new type of cloud native database was introduced in the middle part of last decade. And a new set of analytics workloads has emerged that is powering a transformation within organizations. And it's doing this by putting data at the core of businesses and organizations. You know for years, we marched to the cadence of Moore's law. That was the innovation engine of our industry, but now that's changed it's data plus machine intelligence plus cloud. That's the new innovation cocktail for the technology industry and industries overall. And at the data cloud summit, we'll hear from snowflake executives, founders, technologists, customers, and ecosystems partners. And of course, you're going to hear from interviews on the cube. So let's dig in a little bit more and to help me, are two snowflake experts, Filipe Hoffa is a data cloud advocate and Kent Graziano is a chief technical evangelists post at Snowflake. Gents great to see you. Thanks for coming on. >> Yeah thanks for having us on this is great. >> Thank you. >> So guys, first, I got to congratulate you on getting to this point. You've achieved beyond escape velocity, and obviously one of the most important IPOs of the year, but you got a lot of work to do I know that Filipe, let me start with you data cloud. What's a data cloud and what are we going to learn about it at the data cloud summit? >> Oh, that's an excellent question. And let me tell you a little bit about our story here. And I really, really, really admire what Kent has done. I joined the snowflake like less than two months ago, and for me it's been a huge learning experience. And I look up to Kent a lot on how we deliver the message and how do we deliver all of that. So I would love to hear his answer first. >> Okay, that's cool. Okay Kent later on. So talk of data cloud, that's a catchy phrase, right? But it vectors into at least two of the components of my innovation, innovation cocktail. What, what are the substantive substantive aspects behind the data cloud? >> I mean, it's a, it's a new concept, right? We've been talking about infrastructure clouds and SAS applications living in an application clouds so data cloud is the ability to really share all that data that we've been collecting. You know, we've, we've spent what, how many days a decade or more with big data now, but have we been able to use it effectively? And that's, that's really where the data cloud is coming in and snowflake in making that a more seamless, friendly, easy experience to get access to the data. I've been in data warehousing for nearly 30 years now. And our dream has always been to be able to augment an organization's analytics with data from outside their organization. And that's just been a massive pain in the neck with having to move files around and replicate the data and maybe losing track of where it came from or where it went. And the data cloud is really giving our customers the ability to do that in a much more governed way, a much more seamless way, and really make it push button to give anyone access to the data they need and have the performance to do the analytics in near real time. It's it's total game changer as, as you already know, and just it's crazy what we're able to do today, compared to what we could do when I started out in my career. >> Well, I'm going to come back to that cause I want to tap your historical perspective, but Filipe, let me ask you. So why did you join snowflake? You're you're the newbie here. What attracted you? >> Exactly, I'm the newbie. I used to work at Google until August. I was there for 10 years. I was a developer advocate there also for data, you might have heard about a big query. I was doing a lot of that and then as time went by, Snowflake started showing up more and more in my feeds, within my customers, in my community. And it came the time. When, I felt that like, you know, when wherever you're working, once in a while you think I should leave this place, I should try something new. I should move my career forward. While at Google, I thought that so many times as anyone would do, and it was only when snowflake showed up, like where snowflake is going now, how snowflake is, is being received by all the customers that I saw this opportunity. And I decided that moving to Snowflake would be a step forward for me. And so far I'm pretty happy. Like the timing has been incredible, but more than the timing and everything, it's really, really a great place for data. What I love first is data sharing data, analyzing data and how Snowflake is doing it it promotes me in phenomena. >> So Ken, I want to come back to you and I say, tap, maybe your historical perspective here. And you said, you know, it's always been a dream that you could do these other things bring in external data. I would say this, that I don't want to push a little bit on this because I have often said that the EDW marketplace really never lived up to its promises of 360 degree views of the customer in real time or near real time analytics. And, and it really has been, as you kind of described are a real challenge for a lot of organizations when Hadoop came in you know, we had, we we we got excited that it was kind of going to actually finally live up to that vision and and and we duped it a lot. And it don't get me wrong. I mean, the whole concept of, you know, bring the compute to data and the lowering the cost and so forth, but it certainly didn't minimize complexity. And, and it seems like, feels like Snowflake is on the cusp of actually delivering that promise that we've been talking about for 30 years. I wonder if you could share your perspective, is it, are we going to get there this time? >> Yeah. And as far as I can tell working with all of our customers, some of them are there. I mean, they're, they Fought through those struggles that you were talking about that I saw throughout my career and now with getting on Snowflake they're, they're delivering customer 360, they're integrating weblogs and IOT data with structured data from their ERP systems or CRM systems, their supply chain systems. And it really is coming to fruition. I mean, the, you know, the industry leaders, you know, Bill Inman and Claudia M Hoff, they've had this vision the whole time, but the technology just wasn't able to support it. And the cloud, as we said about the internet, changed everything and then Ben Y and Terry, in their vision and building the system, taking the best concepts from the Hadoop world and the data Lake world and the enterprise data warehouse world, and putting it all together into this, this architecture, that's now, you know Snowflake and the data cloud solved it. I mean, it's the, you know, the, the classic benefit of her insight is 2020 after years in the industry, they had seen these problems and said like, how can we solve them? Does the cloud let us solve these problems? And the answer was yes, but it did require writing everything from scratch and starting over with because the architecture the cloud just allows you to do things that you just couldn't do before. Yeah I'm glad you brought up, you know, some of the originators of the data warehouse, because it really wasn't their fault. They were trying to solve a problem. That was the marketers that took it and really kind of made promises that they couldn't keep. But the reality is when you talk to customers in the, in the, so the old EDW days, and this is the other thing I want to, I want to tap your guys' brains on. It was very challenging. I mean, one, one customer, one time referred to it as a snake, swallowing a basketball. And what he meant by that is you know, every time there's a change, you know, Sarbanes Oxley comes and we have to ingest all this new data. It's like, Oh, it's just everything slows down to a grinding halt. Every time Intel came out with a new microprocessor, they would go out and grab a new server as fast as they possibly could. He called it chasing the chips, and it was this endless cycle of pain. And so, you know, the originators of the data whereas they didn't, they didn't have you know the compute power, they didn't have the cloud. >> Yeah. >> And so, and of course they didn't have the 30- 40 years of pain to draw upon. But, but I wonder if you could, could maybe talk a little bit about the kinds of things that can be done now that we haven't been able to do here before. >> Well, yeah I remember early on having a conversation with, with Bill about this idea of near real time data warehousing and saying, is this real? Is this something really need people need? And at the time it was, was a couple of decades ago, he said no to them they just want to load their data sooner than once a month. >> Yeah. >> That was the goal. And that was going to be near real time for them. And, but now I'm seeing it with our customers. It's like, now we can do it, you know, with things like the Kafka technology and snow pipe in, in Snowflake, that people are able to get that refresh way faster and have near real time analytics access to that data in a much more timely manner. And so it really is coming true. And the, the compute power that's there, as you said, you know we, we've now got this compute power in the cloud that we never dreamed of. I mean, you would think of only certain very large, massive global companies or governments could afford supercomputers. And that's what it would have taken. And now we've got nearly the power of a supercomputer in our mobile device that we all carry around with us. So being able to harness all that now in the cloud is really opening up opportunities to do things with data and access data in a way that again really we just kind of dreamed of before. It's like, we can, we can democratize data when we get to this point. And I think that's the, that's where we are, we're at that inflection point where now it's, it's possible to do it. So the challenge on organizations is going to be, how do we do it effectively? How do we do it with agility? And how do we do it in a governed manner? You mentioned Sarbanes Oxley, GDPR, CCPA, all of those are out there. And so we have all of that as well. And so that's where, that's where we're going to get into it, right. Is into the governance and being able to do that in a very quick, flexible, extensible manner and you know, Snowflakes really letting people do it now. >> Well, yeah and you know, again, we've been talking about Hadoop and again, for all my, my fond thoughts of that era, and it's not like hadoop is gone, but, but it was a lot of excitement around it but but governance was a huge problem and it was kind of a ball tough enough. Felipe I got to ask you, like when you think about a company like Google your former employer, you know, data is at the core of their business. And so many companies, the data is not at the core of their business. Something else is it's a process or a manufacturing facility or you know whatever it is. And the data is sort of on the outskirts. You know, we often talk about in, in stove pipes. And so we're now seeing organizations really put data at the core of their it becomes, you know, central to their, to their DNA. I'm curious as to your thoughts on that. And also if you've got a lot of experience with developers, is there, is there a developer angle here in this new data world? >> Oh, for sure. I mean, I love seeing every, like throughout my career at Google and my two months here and talking to so many companies, you never thought before, like these are database companies, but the the ones that keep rowing. The ones that keep moving to the next stage of their development is because they are focusing on data. They are adapting the processes they learning from it. And me, I focus a lot on developers. So I mean when I started This career as an advocate. First I was a software engineer and my work so far, has been work, I really loved talking to the engineers on the other companies. Like maybe I'm not the one solving the business problem, but at the end of the day, when these companies have a business problem that they want to row, they want to have data. There are other engineers that are scientists likes me that are, that, that want to work for work for the company and bring the best technology to solve the problems. Yeah, there's so much where data can help as we evolve the system for the company. And also for us for understanding the systems, things like observability and recently, there was a big company, a big launch on observability the company name is observable, where they are running all of their data warehousing needs. And all of their data needs on Snowflake, just because running these massive systems and being able to see how they're working generates a lot of data. And then how do you manage it? How do you analyze it? Or snowflake is already there to help. >> Well you know >> I covered the two areas. >> It's interesting my, my business partner, John farrier, cohost of the cube, he said, gosh, I would say middle of the last decade, maybe even around the time, you know, 2013, when Snowflake was just coming out, he said, he predicted the data would be the new development kit. And you know, it's really at the center of a lot of, you know, the data life cycle, the, the, what I call the data pipelines. I know people use that term differently, but, but I'm, I'm very excited about the data cloud summit and what we're going to learn there. And I get to interview a lot of really cool people. And so I appreciate you guys coming on, but Kent, who, who should attend the data cloud summit, I mean, what, what are the, what should they expect to learn? >> Well, as you said earlier, Dave, there's, there's so many tracks and there's really kind of something for everyone. So we've got a track on unlocking the value of the data cloud, which is really going to speak to, you know, the business leaders, you know, as to what that vision is, what can we do from an organizational perspective, with the data cloud to get that value from the data to, to move our businesses forward. But we've also got, you know, for the technicians migrating to Snowflake training sessions on how to do the migration, modernizing your data like data science, you know how to do analytics with the, and data science in Snowflake and in the data cloud and even down to building apps. So the developers and building data products. So, you know, we've got stuff for developers, we've got stuff for data scientists. We've got stuff for the, the data architects like myself and the data engineers on how to, how to build all of this out. And then there's going to be some industry solutions spotlights as well. So we can talk about different verticals of folks in FinTech and, and in healthcare. There's going to be stuff for them. And then for our, our data superheroes, we have a hallway track where we're going to get talks from the folks that are in our data superheroes, which is really our community advocacy program. So these are folks who are out there in the trenches using Snowflake, delivering value at, at their organizations. And they're going to talk you know down and dirty. How did they make this stuff happen? So there's going to be just really something for everyone, fireside chats with our executives, of course, something I'm really looking forward to in myself. It's always fun to, to hear from Frank and Christian. And Benwah about, you know, what's the next big thing, you know, what are we doing now? Where are we going with all of this? And then there is going to be some awards. We'll be giving out our data driver awards for our most innovative customers. So this is going to be a lot, a lot for everybody to consume and enjoy and learn about this, this new space of, of the data cloud. >> Well, thank you for that Kent. And I'll second that, I mean, there's going to be a lot for everybody. If you're an existing Snowflake customer, there's going to be plenty of two on one content we can get in to the how to's and the best practice. If you're really not that familiar with Snowflake, or you're not a customer, there's a lot of one-on-one content going on. If you're an investor and you want to figure out, okay, what is this vision? And can, you know, will this company grow into its massive valuation and how are they going to do that? I think you're going to, you're going to hear about the data cloud and really try get a perspective. And you can make your own judgment as to, to, you know, whether or not you think that it's going to be as large a market as many people think. So Felipe, I'd love to hear from you what people can expect at the data cloud summit. >> Totally, so I would love to plus one to everyone that Kent said. We have a phenomenal schedule that the the executive will be there. And I really wanted to specially highlight the session I'm preparing with Trevor Noah. I'm sure you might have heard of him. And we are having him at the data cloud summit, and we are going to have a session. We're going to talk about data. We are preparing a session, That's all about how people that love data, that people that want to make data actionable. How can they bring storytelling and make it more, have more impact as he has well learned to do through his life. >> That's awesome, So yeah, Trevor Noah, we're not just going to totally geek out here. We're going to, we're going to have some great entertainment as well. So I want you to go to snowflake.com and click on data cloud summit, 2020 there's four geos. It starts on November 17th and then runs through the week and then the following week in Japan. So, so check that out. We'll see you there. This is Dave Volante for the cube. Thanks for watching. (soft music)

>>from around the globe. It's the >>cube presenting >>active de que intelligent automation for data quality brought to you by Iota Ho >>Welcome to the sixth episode of the I. O. Tahoe data automation series. On the cube. We're gonna start off with a segment on how to accelerate the adoption of snowflake with Glenn Grossman, who is the enterprise account executive from Snowflake and yusef khan, the head of data services from Iota. Gentlemen welcome. >>Good afternoon. Good morning, Good evening. Dave. >>Good to see you. Dave. Good to see you. >>Okay glenn uh let's start with you. I mean the Cube hosted the snowflake data cloud summit in November and we heard from customers and going from love the tagline zero to snowflake, you know, 90 minutes very quickly. And of course you want to make it simple and attractive for enterprises to move data and analytics into the snowflake platform but help us understand once the data is there, how is snowflake helping to achieve savings compared to the data lake? >>Absolutely. dave. It's a great question, you know, it starts off first with the notion and uh kind of, we coined it in the industry or t shirt size pricing. You know, you don't necessarily always need the performance of a high end sports car when you're just trying to go get some groceries and drive down the street 20 mph. The t shirt pricing really aligns to, depending on what your operational workload is to support the business and the value that you need from that business? Not every day. Do you need data? Every second of the moment? Might be once a day, once a week through that t shirt size price and we can align for the performance according to the environmental needs of the business. What those drivers are the key performance indicators to drive that insight to make better decisions, It allows us to control that cost. So to my point, not always do you need the performance of a Ferrari? Maybe you need the performance and gas mileage of the Honda Civic if you would just get and deliver the value of the business but knowing that you have that entire performance landscape at a moments notice and that's really what what allows us to hold and get away from. How much is it going to cost me in a data lake type of environment? >>Got it. Thank you for that yussef. Where does Io Tahoe fit into this equation? I mean what's, what's, what's unique about the approach that you're taking towards this notion of mobilizing data on snowflake? >>Well, Dave in the first instance we profile the data itself at the data level, so not just at the level of metadata and we do that wherever that data lives. So it could be structured data could be semi structured data could be unstructured data and that data could be on premise. It could be in the cloud or it could be on some kind of SAAS platform. And so we profile this data at the source system that is feeding snowflake within snowflake itself within the end applications and the reports that the snowflake environment is serving. So what we've done here is take our machine learning discovery technology and make snowflake itself the repository for knowledge and insights on data. And this is pretty unique. Uh automation in the form of our P. A. Is being applied to the data both before after and within snowflake. And so the ultimate outcome is that business users can have a much greater degree of confidence that the data they're using can be trusted. Um The other thing we do uh which is unique is employee data R. P. A. To proactively detect and recommend fixes the data quality so that removes the manual time and effort and cost it takes to fix those data quality issues. Uh If they're left unchecked and untouched >>so that's key to things their trust, nobody's gonna use the data. It's not trusted. But also context. If you think about it, we've contextualized are operational systems but not our analytic system. So there's a big step forward glen. I wonder if you can tell us how customers are managing data quality when they migrate to snowflake because there's a lot of baggage in in traditional data warehouses and data lakes and and data hubs. Maybe you can talk about why this is a challenge for customers. And like for instance can you proactively address some of those challenges that customers face >>that we certainly can. They have. You know, data quality. Legacy data sources are always inherent with D. Q. Issues whether it's been master data management and data stewardship programs over the last really almost two decades right now, you do have systemic data issues. You have siloed data, you have information operational, data stores data marks. It became a hodgepodge when organizations are starting their journey to migrate to the cloud. One of the things that were first doing is that inspection of data um you know first and foremost even looking to retire legacy data sources that aren't even used across the enterprise but because they were part of the systemic long running operational on premise technology, it stayed there when we start to look at data pipelines as we onboard a customer. You know we want to do that era. We want to do QA and quality assurance so that we can, And our ultimate goal eliminate the garbage in garbage out scenarios that we've been plagued with really over the last 40, 50 years of just data in general. So we have to take an inspection where traditionally it was E. T. L. Now in the world of snowflake, it's really lt we're extracting were loading or inspecting them. We're transforming out to the business so that these routines could be done once and again give great business value back to making decisions around the data instead of spending all this long time. Always re architect ng the data pipeline to serve the business. >>Got it. Thank you. Glenda yourself of course. Snowflakes renowned for customers. Tell me all the time. It's so easy. It's so easy to spin up a data warehouse. It helps with my security. Again it simplifies everything but so you know, getting started is one thing but then adoption is also a key. So I'm interested in the role that that I owe. Tahoe plays in accelerating adoption for new customers. >>Absolutely. David. I mean as Ben said, you know every every migration to Snowflake is going to have a business case. Um uh and that is going to be uh partly about reducing spending legacy I. T. Servers, storage licenses, support all those good things um that see I want to be able to turn off entirely ultimately. And what Ayatollah does is help discover all the legacy undocumented silos that have been built up, as Glenn says on the data estate across a period of time, build intelligence around those silos and help reduce those legacy costs sooner by accelerating that that whole process. Because obviously the quicker that I. T. Um and Cdos can turn off legacy data sources the more funding and resources going to be available to them to manage the new uh Snowflake based data estate on the cloud. And so turning off the old building, the new go hand in hand to make sure those those numbers stack up the program is delivered uh and the benefits are delivered. And so what we're doing here with a Tahoe is improving the customers are y by accelerating their ability to adopt Snowflake. >>Great. And I mean we're talking a lot about data quality here but in a lot of ways that's table stakes like I said, if you don't trust the data, nobody's going to use it. And glenn, I mean I look at Snowflake and I see obviously the ease of use the simplicity you guys are nailing that the data sharing capabilities I think are really exciting because you know everybody talks about sharing data but then we talked about data as an asset, Everyone so high I to hold it. And so sharing is is something that I see as a paradigm shift and you guys are enabling that. So one of the things beyond data quality that are notable that customers are excited about that, maybe you're excited about >>David, I think you just cleared it out. It's it's this massive data sharing play part of the data cloud platform. Uh you know, just as of last year we had a little over about 100 people, 100 vendors in our data marketplace. That number today is well over 450 it is all about democratizing and sharing data in a world that is no longer held back by FTp s and C. S. V. S and then the organization having to take that data and ingested into their systems. You're a snowflake customer. want to subscribe to an S and P data sources an example, go subscribe it to it. It's in your account there was no data engineering, there was no physical lift of data and that becomes the most important thing when we talk about getting broader insights, data quality. Well, the data has already been inspected from your vendor is just available in your account. It's obviously a very simplistic thing to describe behind the scenes is what our founders have created to make it very, very easy for us to democratize not only internal with private sharing of data, but this notion of marketplace ensuring across your customers um marketplace is certainly on the type of all of my customers minds and probably some other areas that might have heard out of a recent cloud summit is the introduction of snow park and being able to do where all this data is going towards us. Am I in an ale, you know, along with our partners at Io Tahoe and R. P. A. Automation is what do we do with all this data? How do we put the algorithms and targets now? We'll be able to run in the future R and python scripts and java libraries directly inside Snowflake, which allows you to even accelerate even faster, Which people found traditionally when we started off eight years ago just as a data warehousing platform. >>Yeah, I think we're on the cusp of just a new way of thinking about data. I mean obviously simplicity is a starting point but but data by its very nature is decentralized. You talk about democratizing data. I like this idea of the global mesh. I mean it's very powerful concept and again it's early days but you know, keep part of this is is automation and trust, yussef you've worked with Snowflake and you're bringing active D. Q. To the market what our customers telling you so far? >>Well David the feedback so far has been great. Which is brilliant. So I mean firstly there's a point about speed and acceleration. Um So that's the speed to incite really. So where you have inherent data quality issues uh whether that's with data that was on premise and being brought into snowflake or on snowflake itself, we're able to show the customer results and help them understand their data quality better Within Day one which is which is a fantastic acceleration. I'm related to that. There's the cost and effort to get that insight is it's a massive productivity gain versus where you're seeing customers who've been struggling sometimes too remediate legacy data and legacy decisions that they've made over the past couple of decades, so that that cost and effort is much lower than it would otherwise have been. Um 3rdly, there's confidence and trust, so you can see Cdos and see IOS got demonstrable results that they've been able to improve data quality across a whole bunch of use cases for business users in marketing and customer services, for commercial teams, for financial teams. So there's that very quick kind of growth in confidence and credibility as the projects get moving. And then finally, I mean really all the use cases for the snowflake depend on data quality, really whether it's data science, uh and and the kind of snow park applications that Glenn has talked about, all those use cases work better when we're able to accelerate the ri for our joint customers by very quickly pushing out these data quality um insights. Um And I think one of the one of the things that the snowflake have recognized is that in order for C. I. O. Is to really adopt enterprise wide, um It's also as well as the great technology with Snowflake offers, it's about cleaning up that legacy data state, freeing up the budget for CIA to spend it on the new modern day to a state that lets them mobilise their data with snowflake. >>So you're seeing the Senate progression. We're simplifying the the the analytics from a tech perspective. You bring in Federated governance which which brings more trust. Then then you bring in the automation of the data quality piece which is fundamental. And now you can really start to, as you guys are saying, democratized and scale uh and share data. Very powerful guys. Thanks so much for coming on the program. Really appreciate your time. >>Thank you. I appreciate as well. Yeah.

>>Hello, buddy. And welcome back to the cubes. Coverage of Snowflake Data Cloud Summer 2020. We're tracking the rise of the data cloud and fresh off the keynotes. Hear Frank's Luqman, the chairman and CEO of Snowflake, and Anita Lynch, the vice president of data governance at Disney Streaming Services. Folks. Welcome E Need a Disney plus. Awesome. You know, we signed up early. Watched all the Marvel movies. Hamilton, the new Pixar movie Soul. I haven't gotten to the man DeLorean yet. Your favorite, but I really appreciate you guys coming on. Let me start with Frank. I'm glad you're putting forth this vision around the data cloud because I never liked the term Enterprise Data Warehouse. What you're doing is is so different from the sort of that legacy world that I've known all these years. But start with why the data cloud? What problems are you trying to solve? And maybe some of the harder challenges you're seeing? >>Yeah, I know. You know, we have We've come a long way in terms of workload execution, right? In terms of scale and performance and, you know, concurrent execution. We really taking the lid off sort of the physical constraints that that have existed on these types of operations. But there's one problem, uh, that were not yet, uh solving. And that is the silo ing and bunkering of data. Essentially, you know, data is locked in applications. It's locked in data centers that's locked in cloud cloud regions incredibly hard for for data science teams to really, you know, unlocked the true value of data. When you when you can address patterns that that exists across data set. So we're perpetuate, Ah, status we've had for for ever since the beginning off computing. If we don't start Thio, crack that problem now we have that opportunity. But the notion of a data cloud is like basically saying, Look, folks, you know, we we have to start inside, lowing and unlocking the data on bring it into a place where we can access it. Uh, you know, across all these parameters and boundaries that have historically existed, it's It's very much a step level function. Customers have always looked at things won't workload at that time. That mentality really has to go. You really have to have a data cloud mentality as well as a workload orientation towards towards managing data. Yeah, >>Anita is great here in your role at Disney, and you're in your keynote and the work. You're doing the governance work, and you're you're serving a great number of stakeholders, enabling things like data sharing. You got really laser focused on trust, compliance, privacy. This idea of a data clean room is really interesting. You know, maybe you can expand on some of these initiatives here and share what you you're seeing as some of the biggest challenges to success. And, of course, the opportunities that you're unlocking. >>Sure. I mean, in my role leading data to governance, it's really critical to make sure that all of our stakeholders not only know what data is available and accessible to them, they can also understand really easily and quickly whether or not the data that they're using is for the appropriate use case. And so that's a big part of how we scale data governance. And a lot of the work that we would normally have to do manually is actually done for us through the data. Clean rooms. >>Thank you for that. I wonder if you could talk a little bit more about the role of data and how your data strategy has evolved and maybe discuss some of the things that Frank mentioned about data silos. And I mean, obviously you can relate to that having been in the data business for a while, but I wonder if you could elucidate on that. >>Sure, I mean data complexities air going to evolve over time in any traditional data architecture. Er, simply because you often have different teams at different periods in time trying thio, analyze and gather data across Ah, whole lot of different sources. And the complexity that just arises out of that is due to the different needs of specific stakeholders, their time constraints. And quite often, um, it's not always clear how much value they're going to be able to extract from the data at the outset. So what we've tried to do to help break down the silos is allow individuals to see up front how much value they're going to get from the data by knowing that it's trustworthy right away. By knowing that it's something that they can use in their specific use case right away, and by ensuring that essentially, as they're continuing to kind of scale the use cases that they're focused on. They're no longer required. Thio make multiple copies of the data, do multiple steps to reprocess the data. And that makes all the difference in the world, >>for sure. I mean, copy creep, because it be the silent killer. Frank, I followed you for a number of years. You know, your big thinker. You and I have had a lot of conversations about the near term midterm and long term. I wonder if you could talk about you know, when you're Kino. You talk about eliminating silos and connecting across data sources, which really powerful concept. But really only if people are willing and able to connect and collaborate. Where do you see that happening? Maybe What are some of the blockers there? >>Well, there's there's certainly, ah natural friction there. I still remember when we first started to talk to to Salesforce, you know, they had discovered that we were top three destination off sales first data, and they were wondering, you know why that was. And and the reason is, of course, that people take salesforce data, push it to snowflake because they wanna overlay it with what data outside of Salesforce. You know, whether it's adobe or any other marketing data set. And then they want to run very highly skilled processes, you know, on it. But the reflexes in the world of SAS is always like, no, we're an island were planning down to ourselves. Everybody needs to come with us as opposed to we We go, you know, to a different platform to run these type of processes. It's no different for the for the public club. Venter Day didn't mean they have, you know, massive moats around there. Uh, you know, their stories to, you know, really prevent data from from leaving their their orbit. Eso there is natural friction in in terms off for this to happen. But on the other hand, you know, there is an enormous need, you know, we can't deliver on on the power and potential of data unless we allow it to come together. Uh, snowflake is the platform that allows that to happen. You know, we were pleased with our relationship with Salesforce because they did appreciate you know why this was important and why this was necessary. And we think you know, other parts of the industry will gradually come around to it as well. So the the idea of a data cloud has really come, right? People are recognizing, you know, why does this matters now? It's not gonna happen overnight, And there's a step global function of very big change in mentality and orientation. You know, >>it's almost as though the SAS ification of our industries sort of repeated some of the application silos, and you build a hardened top around it. All the processes are hardened around it, and Okay, here we go. And you're really trying to break that, aren't you? Yeah, Exactly. Anita. Again, I wanna come back to this notion of governance. It's so it's so important. It's the first role in your title, and it really underscores the importance of this. Um, you know, Frank was just talking about some of the hurdles, and and this is this is a big one. I mean, we saw this in the early days of big data. Where governance was this after thought it was like, bolted on kind of wild, Wild West. I'm interested in your governance journey, and maybe you can share a little bit about what role Snowflake has played there in terms of supporting that agenda. Bond. Kind of What's next on that journey? >>Sure. Well, you know, I've I've led data teams in a numerous, uh, in numerous ways over my career. This is the first time that I've actually had the opportunity to focus on governance. And what it's done is allowed for my organization to scale much more rapidly. And that's so critically important for our overall strategy as a company. >>Well, I mean a big part of what you were talking about, at least my inference in your your talk was really that the business folks didn't have to care about, you know, wonder about they cared about it. But they're not the wonder about and and about the privacy, the concerns, etcetera. You've taken care of all that. It's sort of transparent to them. Is that >>yeah, right. That's right. Absolutely. So we focus on ensuring compliance across all the different regions where we operate. We also partner very heavily with our legal and information security teams. They're critical to ensuring, you know, that we're able Thio do this. We don't We don't do it alone. But governance includes not just, you know, the compliance and the privacy. It's also about data access, and it's also about ensuring data quality. And so all of that comes together under the governance umbrella. I also lead teams that focus on things like instrumentation, which is how we collect data. We focus on the infrastructure and making sure that we've architected for scale and all of these air really important components of our strategy. >>I got. So I have a question. Maybe each of you can answer. I I sort of see this our industry moving from, you know, products. So then the platforms and platforms even involving into ecosystems. And then there's this ecosystem of of data. You guys both talked a lot about data sharing. But maybe Frank, you could start in Anita. You can add on to Frank's answer. You're obviously both both passionate about the use of of data and trying to do so in a responsible way. That's critical, but it's also gonna have business impact. Frank, where's this passion come from? On your side. And how are you putting in tow action in your own organization? >>Well, you know, I'm really gonna date myself here, but, you know, many, many years ago, you know, I saw the first glimpse off, uh, multidimensional databases that were used for reporting. Really, On IBM mainframes on debt was extraordinarily difficult. We didn't even have the words back then. In terms of data, warehouses and business. All these terms didn't exist. People just knew that they wanted to have, um, or flexible way of reporting and being able Thio pivot data dimensionally and all these kinds of things. And I just whatever this predates, you know, Windows 3.1, which, really, you know, set off the whole sort of graphical in a way of dealing with systems which there's not a whole generations of people that don't know any different. Right? So I I've lived the pain off this problem on sort of been had a front row seat to watching this This transpire over a very long period of time. And that's that's one of the reasons um, you know why I'm here? Because I finally seen, you know, a glimpse off, you know, also as an industry fully fully just unleashing and unlocking the potential were not in a place where the technology is ahead of people's ability to harness it right, which we've We've never been there before, right? It was always like we wanted to do things that technology wouldn't let us. It's different now. I mean, people are just heads are spinning with what's now possible, which is why you see markets evolved very rapidly right now. Way we were talking earlier about how you can't take, you know, past definitions and concepts and apply them to what's going on the world. The world's changing right in front of your eyes right now. >>Sonita. Maybe you could add on to what Frank just said and share some of the business impacts and and outcomes that air notable since you're really applied your your love of data and maybe maybe touch on culture, your data culture. You know any words of wisdom for folks in the audience who might be thinking about embarking on a data cloud journey similar to what you've been on? >>Yeah, sure, I think for me. I fell in love with technology first, and then I fell in love with data, and I fell in love with data because of the impact the data can have on both the business and the technology strategy. And so it's sort of that nexus, you know, between all three and in terms of my career journey and and some of the impacts that I've seen I mean, I think with the advent of the cloud, you know before, Well, how do I say that before the cloud actually became, you know, so prevalent in such a common part of the strategy that's required? It was so difficult, you know, so painful. It took so many hours to actually be able to calculate, you know, the volumes of data that we had. Now we have that accessibility, and then on top of it with the snowflake data cloud, it's much more performance oriented from a cost perspective because you don't have multiple copies of the data, or at least you don't have toe have multiple copies of the data. And I think moving beyond some of the traditional mechanisms for for measuring business impact has has only been possible with the volumes of data that we have available to us today. And it's just it's phenomenal to see the speed at which we can operate and really, truly understand our customers, interests and their preferences, and then tailor the experiences that they really want and deserve for them. Um, it's It's been a great feeling. Thio, get to this point in time. >>That's fantastic. So, Frank, I gotta ask you if you're still in your spare time, you decided to write a book? I'm loving it. Um, I don't have a signed copy, so I'm gonna have to send it back and have you sign it. But you're I love the inside baseball. It's just awesome. Eso really appreciate that. So But why did you decide to write a book? >>Well, there were a couple of reasons. Obviously, we thought it was an interesting tale to tell for anybody you know who is interested in, You know what's going on. How did this come about, You know, where the characters behind the scenes and all this kind of stuff. But, you know, from a business standpoint, because this is such a step function, it's so non incremental. We felt like, you know, we really needed quite a bit of real estate to really lay out what the full narrative and context is on. Do you know we thought books titled The Rise of the Data Cloud. That's exactly what it ISS and We're trying to make the case for that mindset, that mentality, that strategy. Because all of us, you know, I think is an industry or were risk off persisting, perpetuating, You know, where we've been since the beginning off computing. So we're really trying to make a pretty forceful case for Look, you know, there is an enormous opportunity out there, The different choices you have to make along the way. >>Guys, we got to leave it there. Frank. I know you and I are gonna talk again. Anita. I hope we have a chance to meet face to face and and talking the Cube live someday. You're phenomenal, guest. And what a great story. Thank you both for coming on. And thank you for watching. Keep it right there. You're watching the Snowflake Data Cloud Summit on the Cube.

>>Hi, I'm Frank's Luqman CEO of Snowflake. And welcome to the Snowflake Data Cloud Summit. I'd like to take the next few minutes to introduce you to >>the data cloud on why it matters to the modern enterprise. As an industry, we have struggled to mobilize our data, meaning that has been hard to put data into service of our enterprises. We're not living in a data economy and for most data central how we run our lives, our businesses and our institutions, every single interaction we have now, whether it's in social media, e commerce or any other service, engagement generates critical data. You multiply this out with the number of actors and transactions. The volume is overwhelming, growing in leaps and bounds every day. There was a time when data operations focused mostly on running reports and populating dashboards to inform people in the enterprise of what had happened on what was going on. And we still do a ton of that. But the emphasis is shifting to data driving operations from just data informing people. There is such a thing as the time value off data meaning that the faster data becomes available, the more impactful and valuable it ISS. As data ages, it loses much of its actionable value. Digital transformation is an overused term in our industry, but the snowflake it means the end to end automation of business processes, from selling to transacting to supporting to servicing customers. Digital processes are entirely disinter mediated in terms of people. Involvement in are driven into end by data. Of course, many businesses have both physical and digital processes, and they are >>intertwined. Think of retail, logistics, delivery services and so on. So a data centric operating discipline is no longer optional data operations Air now the beating heart >>of the modern enterprise that requires a massively scalable data platform talented data engineering and data science teams to fully exploit the technology that now is becoming available. Enter snowflake. Chances are that, you know, snowflake as a >>world class execution platform for a diverse set of workloads. Among them data warehousing, data engineering, data, lakes, data, science, data applications and data sharing. Snowflake was architected from scratch for cloud scale computing. No legacy technology was carried forward in the process. Snowflake reimagined many aspects of data management data operations. The result was a cloud data platform with massive scale, blistering performance, superior economics and world class data governance. Snowflake innovated on a number of vectors that wants to deliver this breakthrough. First scale and performance. Snowflake is completely designed for cloud scale computing, both in terms of data volume, computational performance and concurrent workload. Execution snowflake features numerous distinct innovations in this category, but none stands up more than the multi cluster shared stories. Architectural Removing the control plane from the individual cluster led to a dramatically different approach that has yielded tremendous benefits. But our customers love about Snowflake is to spin up new workloads without limitation and provisioned these workloads with his little or as much compute as they see fit. No longer do they fear hidden capacity limits or encroaching on other workloads. Customers can have also scale storage and compute independent of each other, something that was not possible before second utility and elasticity. Not only can snowflake customer spin up much capacity for as long as they deem necessary. Three. Utility model in church, they only get charged for what they consumed by the machine. Second, highly granular measurement of utilization. Ah, lot of the economic impact of snowflake comes from the fact that customers no longer manage capacity. What they do now is focused on consumption. In snowflake is managing the capacity. Performance and economics now go hand in hand because faster is now also cheaper. Snowflake contracts with the public cloud vendors for capacity at considerable scale, which then translates to a good economic value at the retail level is, well, third ease of use and simplicity. Snowflake is a platform that scales from the smallest workloads to the largest data estates in the world. It is unusual in this offer industry to have a platform that controversy the entire spectrum of scale, a database technology snowflake is dramatically simple fire. To compare to previous generations, our founders were bent on making snowflake, a self managing platform that didn't require expert knowledge to run. The role of the Deba has evolved into snowflake world, more focused on data model insights and business value, not tuning and keeping the infrastructure up and running. This has expanded the marketplace to nearly any scale. No job too small or too large. Fourth, multi cloud and Cross Cloud or snowflake was first available on AWS. It now also runs very successfully on mark yourself. Azure and Google Cloud Snowflake is a cloud agnostic platform, meaning that it doesn't know what it's running on. Snowflake completely abstracts the underlying cloud platform. The user doesn't need to see or touch it directly and also does not receive a separate bill from the cloud vendor for capacity consumed by snowflake. Being multi cloud capable customers have a choice and also the flexibility to change over time snowflakes. Relationships with Amazon and Microsoft also allow customers to transact through their marketplaces and burned down their cloud commit with their snowflakes. Spend Snowflake is also capable of replicating across cloud regions and cloud platforms. It's not unusual to see >>the same snowflake data on more than one public cloud at the time. Also, for disaster recovery purposes, it is desirable to have access to snowflake on a completely different public cloud >>platform. Fifth, data Security and privacy, security and privacy are commonly grouped under the moniker of data governance. As a highly managed cloud data platform, snowflake designed and deploys a comprehensive and coherent security model. While privacy requirements are newer and still emerging in many areas, snowflake as a platform is evolving to help customers steer clear from costly violations. Our data sharing model has already enabled many customers to exchange data without surrendering custody of data. Key privacy concerns There's no doubt that the strong governance and compliance framework is critical to extracting you analytical value of data directly following the session. Police Stay tuned to hear from Anita Lynch at Disney Streaming services about how >>to date a cloud enables data governance at Disney. The world beat a >>path to our door snowflake unleashed to move from UN promised data centers to the public cloud platforms, notably AWS, Azure and Google Cloud. Snowflake now has thousands of enterprise customers averaging over 500 million queries >>today across all customer accounts, and it's one of the fastest growing enterprise software companies in a generation. Our recent listing on the New York Stock Exchange was built is the largest software AIPO in history. But the data cloth conversation is bigger. There is another frontier workload. Execution is a huge part of it, but it's not the entire story. There is another elephant in the room, and that is that The world's data is incredibly fragmented in siloed, across clouds of old sorts and data centers all over the place. Basically, data lives in a million places, and it's incredibly hard to analyze data across the silos. Most intelligence analytics and learning models deploy on single data sets because it has been next to impossible to analyze data across sources. Until now, Snowflake Data Cloud is a data platform shared by all snowflake users. If you are on snowflake, you are already plugged into it. It's like being part of a Global Data Federation data orbit, if you will, where all other data can now be part of your scope. Historically, technology limitations led us to build systems and services that siloed the data behind systems, software and network perimeters. To analyze data across silos, we resorted to building special purpose data warehouses force fed by multiple data sources empowered by expensive proprietary hardware. The scale limitations lead to even more silos. The onslaught of the public cloud opened the gateway to unleashing the world's data for access for sharing a monetization. But it didn't happen. Pretty soon they were new silos, different public clouds, regions within the and a huge collection of SAS applications hoarding their data all in their own formats on the East NC ations whole industries exist just to move data from A to B customer behavior precipitated the silo ing of data with what we call a war clothes at a time mentality. Customers focused on the applications in isolation of one another and then deploy data platforms for their workload characteristics and not much else, thereby throwing up new rules between data. Pretty soon, we don't just have our old Silas, but new wants to content with as well. Meanwhile, the promise of data science remains elusive. With all this silo ing and bunkering of data workload performance is necessary but not sufficient to enable the promise of data science. We must think about unfettered data access with ease, zero agency and zero friction. There's no doubt that the needs of data science and data engineering should be leading, not an afterthought. And those needs air centered on accessing and analyzing data across sources. It is now more the norm than the exception that data patterns transcend data sources. Data silos have no meaning to data science. They are just remnants of legacy computing. Architectures doesn't make sense to evaluate strictly on the basis of existing workloads. The world changes, and it changes quickly. So how does the data cloud enabled unfettered data access? It's not just a function of being in the public cloud. Public Cloud is an enabler, no doubt about it. But it introduces new silos recommendation by cloud, platform by cloud region by Data Lake and by data format, it once again triggered technical grandstands and a lot of programming to bring a single analytical perspective to a diversity of data. Data was not analytics ready, not optimized for performance or efficiency and clearly lacking on data governance. Snowflake, address these limitations, thereby combining great execution with great data >>access. But, snowflake, we can have the best of both. So how does it all work when you join Snowflake and have your snowflake account? You don't just >>avail yourself of unlimited stories. And compute resource is along with a world class execution platform. You also plug into the snowflake data cloud, meaning that old snowflake accounts across clouds, regions and geography are part of a single snowflake data universe. That is the data clouds. It is based on our global data sharing architectures. Any snowflake data can be exposed and access by any other snowflake user. It's seamless and frictionless data is generally not copied. Her moves but access in place, subject to the same snowflake governance model. Accessing the data cloth can be a tactical one on one sharing relationship. For example, imagine how retailer would share data with a consumer back. It's good company, but then it easily proliferate from 1 to 1. Too many too many. The data cloud has become a beehive of data supply and demand. It has attracted hundreds of professional data listings to the Snowflake Data Marketplace, which fuels the data cloud with a rich supply of options. For example, our partner Star Schema, listed a very detailed covert 19 incident and fatality data set on the Snowflake Data Marketplace. It became an instant hit with snowflake customers. Scar schema is not raw data. It is also platform optimize, meaning that it was analytics ready for all snowflake accounts. Snowflake users were accessing, joining and overlaying this new data within a short time of it becoming available. That is the power of platform in financial services. It's common to see snowflake users access data from snowflake marketplace listings like fax set and Standard and Poor's on, then messed it up against for example. Salesforce data There are now over 100 suppliers of data listings on the snowflake marketplace That is, in addition to thousands of enterprise and institutional snowflake users with their own data sets. Best part of the snowflake data cloud is this. You don't need to do or buy anything different. If your own snowflake you're already plugged into the data clouds. A whole world data access options awaits you on data silos. Become a thing of the past, enjoy today's presentations. By the end of it, you should have a better sense in a bigger context for your choices of data platforms. Thank you for joining us.

>> Hello everybody. And welcome back to, theCUBE coverage of the Snowflake Data Cloud Summit 2020. We're tracking the rise of the Data Cloud, and fresh off the keynotes here, Frank Slootman, the Chairman and CEO of Snowflake and Anita Lynch, the Vice President of data governance at Disney streaming services. Folks Welcome. >> Thank you >> Thanks for having us Dave. >> Anita Disney plus awesome. You know, we signed up early, watched all the Marvel movies, Hamilton, the new Pixar movie soul. I haven't gotten it to the Mandalorian yet, your favorite. But really appreciate you guys coming on. Let me start with Frank. I'm glad you're putting forth this vision around the Data Cloud, because I never liked the term enterprise data warehouse. What you're doing is so different from the sort of that legacy world that I've known all these years. But start with why the Data Cloud? What problems are you trying to solve? And maybe some of the harder challenges you're seeing. >> Yeah, you know, we have a, we've come a long way in terms of workload execution. Right? In terms of scale and performance, and concurrent execution. We've really taken the lid off, sort of the physical constraints that have existed on these type of operations. But there's one problem that we're not yet solving, and that is the siloing and bunkering of data. And essentially, data is locked in applications, it's locked in data centers, it's locked in cloud, cloud regions. Incredibly hard for data science teams to really unlock the true value of data, when you can't address patterns that exist across data sets. So where we perpetuate a status we've had for forever since the beginning of computing. If we don't start to crack that problem now we have that opportunity. But the notion of a Data Cloud is like basically saying, "Look folks, we have to start on siloing and unlocking the data, and bring it into a place, where we can access it across all these perimeters, and boundaries that have historically existed. It's very much a step level function. Like the customers have always looked at things, one workload at a time, that mentality really has to go. You really have to have a Data Cloud mentality, as well as a workload orientation towards managing data. >> Anita, it was great hearing your role at Disney and in your keynote, and the work you're doing, the governance work. and you're serving a great number of stakeholders, enabling things like data sharing. You got really laser focused on trust, compliance, privacy. This idea of a data clean room is really interesting. Maybe you can expand on some of these initiatives here, and share what you're seeing as some of the biggest challenges to success, and of course, the opportunities that you're unlocking. >> Sure. In my role leading data governance, it's really critical to make sure that all of our stakeholders not only know what data is available and accessible to them. They can also understand really easily and quickly, whether or not the data that they're using is for the appropriate use case. And so that's a big part of how we scale data governance, and a lot of the work that we would normally have to do manually is actually done for us through the data clean rooms. >> Thank you for that. I wonder if you could talk a little bit more about the role of data and how your data strategy has evolved and maybe discuss some of the things that Frank mentioned about data silos. And I mean, obviously you can relate to that having been in the data business for a while, but I wonder if you can elucidate on that. >> Sure. I mean, data complexities are going to evolve over time in any traditional data architecture simply because you often have different teams at different periods and time trying to analyze and gather data across a whole lot of different sources. And the complexity that just arises out of that is due to the different needs of specific stakeholders. There are time constraints and quite often, it's not always clear how much value they're going to be able to extract from the data at the outset. So what we've tried to do to help break down those silos is allow individuals to see upfront how much value they're going to get from the data by knowing that it's trustworthy right away. By knowing that it's something that they can use in their specific use case right away. And by ensuring that essentially as they're continuing to kind of scale the use cases that they're focused on, they're no longer required to make multiple copies of the data, do multiple steps to reprocess the data. And that makes all the difference in the world. >> Yeah, for sure. I'm a copy Creek because it'd be the silent killer. Frank I followed you for a number of years, you're a big thinker, you and I have had a lot of conversations about the near-term, mid-term and long-term, I wonder if you could talk about, in your keynote you're talking about eliminating silos and connecting across data sources. Which is really powerful concept but really only if people are willing and able to connect and collaborate. Where do you see that happening? Maybe what are some of the blockers there? >> Well, there's certainly a natural friction there. I still remember when we first started to talk to, Salesforce, you know, they had discovered that we were a top three destination of Salesforce data and they were wondering why that was, and the reason is of course, that people take Salesforce data push it to snowflake because they want to overlay it with what data outside of Salesforce. Whether it's Adobe or any other marketing dataset. And then they want to run very highly scaled processes on it. But the reflexes in the world of SaaS is always like no, we're an Island, we're a planet down to ourselves. Everybody needs to come with us, as opposed to we go to a different platform to run these types of processes. It's no different for the public cloud vendor. They didn't only, they have massive moats around their storage to really prevent data from leaving their orbit. So there is natural friction in terms for this to happen. But on the other hand there is an enormous need. We can't deliver on the power and potential of data unless we allow it to come together. Snowflake is the platform that allows that to happen. We were pleased with our relationship with Salesforce because they did appreciate why this was important and why this was necessary. And we think, other parts of the industry will gradually come around to it as well. So the idea of a Data Cloud has really come, right. When people are recognizing why this matters now. It's not going to happen overnight. It is a step while will function a very big change in mentality and orientation. >> Yeah. It's almost as though the the SaaS suffocation of our industry sort of repeated some of the application silos and you build a hardened top around it, all the processes are hardened around it and okay, here we go. And you're really trying to break that, aren't you? >> Yep, exactly. >> Anita, again, I want to come back to this notion of governance. It's so it's so important. It's the first role in your title and it really underscores the importance of this. You know, Frank was just talking about some of the hurdles and this is a big one. I mean, we saw this in the early days of big data where governance was just afterthought. It was like bolted on the kind of wild wild West. I'm interested in your governance journey. And maybe you can share a little bit about what role snowflake has played there in terms of supporting that agenda and kind of what's next on that journey. >> Sure. Well, I've led data teams in numerous ways over my career. This is the first time that I've actually had the opportunity to focus on governance and what it's done is allowed for my organization to scale much more rapidly. And that's so critically important for our overall strategy as a company. >> Well, I mean, a big part of what you were talking about at least my inference in your talk was really that the business folks didn't have to care about, you know, wonder about they cared about it, but they don't have to wonder about, and about the privacy concerns, et cetera. You've taken care of all that it's sort of transparent to them. Is that right?| >> Yea That's right absolutely. So we focus on ensuring compliance across all of the different regions where we operate. We also partner very heavily with our legal and information security teams. They're critical to ensuring that we're able to do this. we don't do it alone. But governance includes not just the compliance and the privacy, it's also about data access, and it's also about ensuring data quality. And so all of that comes together under the governance umbrella. I also lead teams that focus on things like instrumentation, which is how we collect data. We focus on the infrastructure and making sure that we've architected for scale and all of these are really important components of our strategy. >> I got a...So I have a question maybe each of you can answer. I sort of see this, our industry moving from products, to then, to platforms and platforms even evolving into ecosystems. And then there's this ecosystem of data. You guys both talked a lot about data sharing but maybe Frank, you can start, Anita you can add on to Frank's answer. You're obviously both passionate about the use of data and trying to do so in a responsible way. That's critical but it's also going to have business impact. Frank, where's this passion come from on your side. And how are you putting into action in your own organization? >> Well, you know I'm really going to date myself here, but many, many years ago, I saw the first glimpse of multidimensional databases that were used for reporting really on IBM mainframes. And it was extraordinarily difficult. We didn't even have the words back then in terms of data warehouses and business. All these terms didn't exist. People just knew that they wanted to have a more flexible in way of reporting and being able to pivot data dimensionally and all these kinds of things. And I just bought whatever this predates windows 3.1, which really, set off the whole sort of graphical, way of dealing with systems which there's now a whole generations of people that don't know any different right? So I've lived the pain of this problem and sort of had a front row seat to watching this transpire over a very long period of time. And that's one of the reasons, why I'm here, because I finally seen, a glimpse of, I also, as an industry fully, fully just unleashing and unlocking to potential. We're now in a place where the technology is ahead of people's ability to harness it. Which we've never been there before. It was always like, we wanted to do things that technology wouldn't let us. It's different now. I mean, people are just, their heads are spinning with what's now possible, which is why you see markets evolve, very rapidly right now we were talking earlier about how you can't take past definitions and concepts and apply them to what's going on in the world. because the world's changing right in front of your eyes right now. >> So Anita maybe you could add on to what Frank just said and share some of the business impacts and outcomes that are notable since you've really applied your your love of data and maybe, maybe touch on, on culture. Data culture, any words of wisdom for folks in the audience who might be thinking about embarking on a Data Cloud journey, similar to what you've been on. >> Yeah sure. I think for me, I fell in love with technology first and then I fell in love with data. And I fell in love with data because of the impact that data can have on both the business and the technology strategy. And so it's sort of that nexus, between all three. And in terms of my career journey and some of the impacts that I've seen. I mean, I think with the advent of the cloud, before, well, how do I say that. Before the cloud actually became so prevalent and such a common part of the strategy that's required it was so difficult, you know, so painful. It took so many hours to actually be able to calculate the volumes of data that we had. Now we have that accessibility, and then on top of it, with the snowflake Data Cloud it's much more performance oriented from a cost perspective because you don't have multiple copies of the data, or at least you don't have to have multiple copies of the data. And I think moving beyond some of the traditional mechanisms for for measuring business impact, has only been possible with the volumes of data that we have available to us today. And it's just, it's phenomenal to see the speed at which we can operate. And really, truly understand our customer's interests and their preferences and then tailor the experiences that they really want and deserve for them. It's, been a great feeling to get to this point in time. >> That's fantastic. So, Frank, I got to ask you this. So in your spare time you decided to write a book, I'm loving it. I don't have a signed copy so I'm going to have to send it back and have you sign it. But, and you're, I love the inside baseball. It's just awesome. So really appreciate that. So, but why did you decide to write a book? >> Well, there were a couple of reasons, obviously we thought of as an interesting tale to tell for anybody, who is interested in what's going on, how did this come about? Who are the characters behind the scenes and all this stuff. But from a business standpoint because this is such a step function it's so non incremental, we felt like, we really needed quite a bit of real estate to really lay out what the full narrative and context is. And, we thought, the books titled the "Rise of the Data Cloud." That's exactly what it is. And we're trying to make the case for that mindset, that mentality, that strategy because all of us, I think as an industry, were at risk of, persisting, perpetuating where we've been since the beginning of computing. So we're really trying to make a pretty forceful case for a look. There's an enormous opportunity out there but there's some choices you have to make along the way. >> Guys, we got to leave it there. Frank, I know you and I are going to talk again Anita, I hope we have a chance to meet face to face and talk in theCUBE live someday. You're phenomenal guests and what a great story. Thank you both for coming on. And thank you for watching. Keep it right there. You're watching the, Snowflake Data Cloud Summit, on theCUBE.

>>Okay, >>listen, we're gearing up for the start of the snowflake Data Cloud Summit, and we wanna go back to the early roots of Snowflake. We've got some of the founding engineers here. Abdul Monir, Ashish Motive, Allah and Alison Lee There three individuals that were at snowflake in the early years and participated in many of the technical decisions that led to the platform and is making snowflake famous today. Folks, great to see you. Thanks so much for taking some time out of your busy schedules. Hey, it's gotta be really gratifying. Thio, See this platform that you've built, you know, taking off and changing businesses. So I'm sure it was always smooth sailing. Right? There were. There were no debates. Wherever. >>I've never seen an engineer get into the bed. >>Alright, So seriously so take us back to the early days. You guys, you know, choose whoever wants to start. But what was it like early on? We're talking 2013 here, right? >>When I think back to the early days of Snowflake, I just think of all of us sitting in one room at the time. You know, we just had an office that was one room with, you know, 12 or 13 engineers sitting there clacking away on our keyboards, uh, working really hard, turning out code, uh, punctuated by you know, somebody asking a question about Hey, what should we do about this, or what should we do about that? And then everyone kind of looking up from their keyboards and getting into discussions and debates about the work that we're doing. >>So so Abdul it was just kind of heads down headphones on, just coating or e think there was >>a lot of talking and followed by a lot of typing. Andi, I think there were periods of time where where you know, anyone could just walk in into the office and probably out of the office and all the here is probably people, uh, typing away at their keyboards. And one of my member vivid, most vivid memories is actually I used to sit right across from Alison, and there's these huge to two huge monitor monitors between us and I would just here typing away in our keyboard, and sometimes I was thinking and and and, uh and all that type and got me nervous because it seemed like Alison knew exactly what what, what she needed to do, and I was just still thinking about it. >>So she she was just like bliss for for you as a developer engineer was it was a stressful time. What was the mood? So when you don't have >>a whole lot of customers, there's a lot of bliss. But at the same time, there was a lot of pressure on us to make sure that we build the product. There was a time line ahead of us. We knew we had to build this in a certain time frame. Um, so one thing I'll add to what Alison and Abdulle said is we did a lot of white boarding as well. There are a lot of discussions, and those discussions were a lot of fun. They actually cemented what we wanted to build. They made sure everyone was in tune, and and there we have it. >>Yes, so I mean, it is a really exciting time doing any start up. But when you know when you have to make decisions and development, invariably you come to a fork in the road. So I'm curious as to what some of those forks might have been. How you guys decided You know which fork to take. Was there a Yoda in the room that served as the Jedi master? I mean, how are those decisions made? Maybe you could talk about that a little bit. >>Yeah, that's an interesting question. And I think one of a Zai think back. One of the memories that that sticks out in my mind is is this, uh, epic meeting and one of our conference rooms called Northstar. Many of our conference rooms are named after ski resorts because the founders, they're really into skiing. And that's why that's where the snowflake name comes from. So there was this epic meeting and I'm not even sure exactly what topic we were discussing. I think it was It was the sign up flow and and there were a few different options on the table and and and one of the options that that people were gravitating Teoh, one of the founders, didn't like it and and on, and they said a few times that there's this makes no sense. There's no other system in the world that does it this way, and and I think one of the other founders said, uh, that's exactly why we should do it this way. And or at least seriously, consider this option. So I think there was always this, um, this this, uh, this tendency and and and this impulse that that we needed to think big and think differently and and not see the world the way it is but the way we wanted it to be and then work our way backwards and try to make it happen. >>Alison, Any fork in the road moments that you remember. >>Well, I'm just thinking back to a really early meeting with sheesh! And and a few of our founders where we're debating something probably not super exciting to a lot of people outside of hardcore database people, which was how to represent our our column metadata. Andi, I think it's funny that you that you mentioned Yoda because we often make jokes about one of our founders. Teary Bond refer to him as Yoda because he hasn't its tendency to say very concise things that kind of make you scratch your head and say, Wow, why didn't I think of that? Or you know, what exactly does that mean? I never thought about it that way. So I think when I think of the Yoda in the room, it was definitely Terry, >>uh, excuse you. Anything you can add to this, this conversation >>I'll agree with Alison on the you're a comment for short. Another big fork in the road, I recall, was when we changed. What are meta store where we store our own internal metadata? We used >>to use >>a tool called my sequel and we changed it. Thio another database called Foundation TV. I think that was a big game changer for us. And, you know, it was a tough decision. It took us a long time. For the longest time, we even had our own little branch. It was called Foundation DB, and everybody was developing on that branch. It's a little embarrassing, but, you know, those are the kind of decisions that have altered altered the shape of snowflake. >>Yeah. I mean, these air, really, you know, down in the weeds, hardcore stuff that a lot of people that might not be exposed to What would you say was the least obvious technical decision that you had to make it the time. And I wanna ask you about the most obvious to. But what was the what was the one that was so out of the box? I mean, you kind of maybe mentioned it a little bit before, but what if we could double click on that? >>Well, I think one of the core decisions in our architectures the separation of compute and storage on Do you know that is really court architecture. And there's so many features that we have today, um, for instance, data sharing zero copy cloning that that we couldn't have without that architecture. Er, um and I think it was both not obvious. And when we told people about it in the early days, there was definitely skepticism about being able to make that work on being able Thio have that architecture and still get great performance. >>Anything? Yeah, anything that was, like, clearly obvious, that is, Maybe that maybe that was the least and the most that that separation from computing story because it allowed you toe actually take advantage of cloud native. But But was there an obvious one that, you know, it's sort of dogma that you, you know, philosophically lived behind. You know, to this day, >>I think one really obvious thing, um is the sort of no tuning, no knobs, ease of use story behind snowflake. Andi and I say it's really obvious because everybody wants their system to be easy to use. But then I would say there are tons of decisions behind that, that it's not always obvious three implications of of such a choice, right, and really sticking to that. And I think that that's really like a core principle behind Snowflake that that led to a lot of non obvious decisions as a result of sticking to that principle. So, yeah, I >>think to add to that now, now you've gotten us thinking I think another really interesting one was was really, um, should we start from scratch or or should we use something that already exists and and build on top of that? And I think that was one of these, um, almost philosophical kind of stances that we took that that a lot of the systems that were out there were the way they were because because they weren't built for the for the platforms that they were running on, and the big thing that we were targeting was the cloud. And so one of the big stances we took was that we were gonna build it from scratch, and we weren't gonna borrow a single line of code from many other database out there. And this was something that really shocked a lot of people and and many times that this was pretty crazy and it waas. But this is how you build great products. >>That's awesome. All right. She should give you the last word. We got, like, just like 30 seconds left to bring us home >>Your till date. Actually, one of those said shocks people when you talk to them and they say, Wow, you're not You're not really using any other database and you build this entirely yourself. The number of people who actually can build a database from scratch are fairly limited. The group is fairly small, and so it was really a humongous task. And as you mentioned, you know, it really changed the direction off how we design the database. What we what does the database really mean? Tow us right the way Snowflake has built a database. It's really a number of organs that come together and form the body and That's also a concept that's novel to the database industry. >>Guys, congratulations. You must be so proud. And, uh, there's gonna be awesome watching the next next decade, so thank you so much for sharing your stories. >>Thanks, dude. >>Thank you.

>>Hello, everyone. And welcome back to our wall to wall coverage of the data Cloud Summit. This is Dave a lot. And we're seeing the emergence of a next generation workload in the cloud were more facile access and governed. Sharing of data is accelerating. Time to insights and action. All right, allow me to introduce our next guest. Amy Irwin is here. She's the vice president of strategy for experience. And Matt Glickman is VP customer product strategy it snowflake with an emphasis on financial services. Folks, welcome to the Cube. Thanks so much for coming on. >>Thanks for >>having us >>nice to be here. Hey, >>So, Amy, I mean, obviously 2020 has been pretty unique and crazy and challenging time for a lot of people. I don't know why I've been checking my credit score a lot more for some reason. On the app I love the app I got hacked. I had a lock it the other day I locked my credit. Somebody tried to dio on and it worked. I was so happy. So thank you for that. But so we know experience, but there's a ton of data behind what you do. I wonder if you could share kind of where you sit in the data space and how you've seen organizations leverage data up to this point. And really, if you could address maybe some of the changes that you're seeing as a result of the pandemic, that would be great. >>Sure, sure. Well, Azaz, you mentioned experience Eyes best known as a credit bureau. Uh, I work in our marketing services business unit, and what we do is we really help brands leverage the power of data and technology to make the right marketing decisions and better understand and connect with consumers. Eso we offer markers products around data identity activation measurement. We have a consumer view data file that's based on off line P I and contains demographic interest, transaction data and other attributes on about 300 million people in the U. S. Uh, and on the identity side, we've always been known for our safe haven or privacy friendly matching that allows marketers to connect their first party data to experience or other third parties. Uh, but in today's world, with the growth and importance of digital advertising and consumer behavior shifting to digital, uh, experience also is working to connect that offline data to the digital world for a complete view of the customer you mentioned co vid, um, we actually we serve many different verticals. And what we're seeing from our clients during co vid is that there's a bearing impact of the pandemic. The common theme is that those that have successfully pivoted their businesses to digital are doing much better. Uh, as we all know, Kobe accelerated very strong trends to digital both in the commerce and immediate viewing habits. We work with a lot of retailers. Retail is a tale of two cities with big box and grocery growing and apparel retail really struggling. We've helped our clients leveraging our data to better understand the shifts in these consumer behaviors and better segment their customers during this really challenging time. Eso think about there's there's a group of customers that is still staying home that is sheltered in place. There's a group of customers starting that significantly varied their consumer behavior, but it's starting to venture out a little. And then there's a group of customers that's doing largely what they did before and a somewhat modified fashion. So we're helping our clients segment those customers into groups to try and understand the right messaging and right offers for each of those groups. And we're also helping them with at risk audiences. Eso That's more on the financial side. Which of your customers air really struggling? Do the endemic And how do you respond? >>It's awesome, thank you. You know, it's it's funny. I mean somebody I saw Twitter poll today asking if we measure our screen time and I said, Oh my no eso Matt, let me ask you. You spend a ton of time in financial services. You really kind of cut your teeth there, and it's always been very data oriented. You've seen a lot of changes tell us about how your customers are bringing together data, the skills that people obviously a big part of the equation and applications to really put data at the center of their universe. What's new and different that these companies were getting out of the investments in data and skills. >>That's a great question. Um, the acceleration that Amy mentioned Israel, Um, we're seeing it particularly this year, but I think even in the past few years, the reluctance of customers to embrace the cloud is behind us. And now there's this massive acceleration to be able to go faster on, and in some ways the new entrance into this category. Have an advantage versus, you know, the companies that have been in the space within its financial services or beyond. Um, and in a lot of ways they are are seeing the cloud and services like snowflake as a way toe not only catch up but leapfrog your competitors and really deliver a differentiated experience to your customers to your business, internally or externally. Um, and this past, you know, however long this crisis has been going on, has really only accelerated that, because now there's a new demand. Understand your customer better your your business better with with your traditional data sources and also new alternative data sources, Um, and also be able to take a pulse. One of things that we learned which was you know, I opening experience was as the crisis unfolded, one of our data partners decided to take the data sets about where the cases where were happening from the Johns Hopkins and World Health Organization and put that on our platform, and it became a runaway hit where now with thousands of our customers overnight, we're using this data to understand how their business was doing versus how the crisis was unfolding in real time. On this has been a game changer, and I think it's only it's only scratching the surface of what now the world will be able to do when data is really at their fingertips. You're not hindered by your legacy platforms. >>I wrote about that back in the early days of the pandemic when you guys did that and talked about some of the changes that you guys enabled and and, you know you're right about Cloud. I mean, financial services. Cloud used to be an evil word, and now it's almost become a mandate. Amy, I >>wonder if you >>could tell us a little bit more about what? What, you know your customers they're having to work through in order to achieve some of these outcomes. I mean, I'm interested in the starting point. I've been talking a lot and writing a lot on talking to practitioners about what I call the data lifecycle. Sometimes people call it the data pipeline. It za complicated matter, but those customers and companies that can put data at the center and really treat that pipeline is the heart of their organization, If you will, really succeeding. What are you seeing and what really is the starting point there? >>Yes, yes, that's a good question. And as you mentioned, first party, I mean, we start with first party data. Right? First party data is critical to understanding consumers on been in different verticals, different companies. Different brands have varying levels of first party data. So retailers gonna have a lot more first party data financial services company, then say an auto manufacturer. Uh, while many marketers have that first party data to really have a 3 60 view of the customer, they need third party data as well. And that's where experience comes in. We help brands connect those disparate data sets both 1st and 3rd party baked data to better understand consumers and create a single customer view, which has a number of applications. I think the last that I heard was that there's about eight devices on average per person. I always joke that we're gonna have these enormous. I mean, that that number is growing. We're gonna have these enormous charging stations in our house, and I think we're because all the different devices and way seamlessly move from device to device along our customer journey. And, um, if the brand doesn't understand who we are, it's much harder for the brand to connect with consumers and create a positive customer experience and way site that about 95% of companies are actually that they are looking to achieve that single customer view. They recognize, um, that they need that. And they've aligned various teams from e commerce to marketing to sales toe at a minimum in just their first party data and then connect that data to better understand, uh, consumers so consumers can interact with the brand through website and mobile app in store visits, um, by the phone, TV ads, etcetera. And a brand needs to use all of those touchpoints often collected by different parts of the organization and then adding that third party data to really understand the consumers in terms of specific use cases, Um, there's there's about three that come to mind, so there's first. There's relevant advertising and reaching the right customer. There's measurement s or being able to evaluate your advertising efforts. Uh, if you see an ad on the if I see it out of my mobile and then I by by visiting a desktop website understanding or get a direct mail piece, understanding that those connect those interactions are all connected to the same person is critical for measurement. And then there's, uh, there's personalization, um, which includes encourage customer experience amongst your own, um, touch points with that consumer personalized marketing communication and then, of course, um, analytics. So those are the use cases we're seeing? Great. >>Thank you, Amy. I'm out. You can't really talk about data without talking about, >>you know, >>governance and and and compliance. And I remember back in 2006, when the Federal Rules of Civil Procedure went in, it was easy. The lawyers just said, No, nobody can have access, but that's changed. One of things I like about what snowflakes doing with the data cloud is it's really about democratizing access, but doing so in a way that gives people confidence that they only have access to the right data. So maybe you could talk a little bit about how you're thinking about this topic, what you're doing to help customers navigate, which has traditionally been such a really challenging problem. >>No, it's another great question. Um, this is where I think the major disruption is happening. Um, and what Amy described being able to join together 1st and 3rd party data sets. Um, being able to do this was always a challenge because data had to be moved around, had a ship, my first party data to the other side. The third party data had to be shipped to me on being able to join those data sets together, um was problematic at best. And now, with the focus on privacy and protecting P, I, um, this is this is something that has to change. And the good news is with the data cloud data does not have to move. Data can stay where it belongs. Experiencing keep its data experience. Customers can hold on to their data. Yet the data can be joined together on this universal global platform that we call the data cloud. On top of that, and particularly with the regulations that are coming out that are gonna prevent data from being collected on either a mobile device or in wet warren as cookies and Web browsers, new approaches. And we're seeing this a lot in our space, both in financials and in media is to set up these data clean rooms where both sides can give access to one another, but not have to reveal any P i i to do that joint. Um, this is gonna be huge right now. You actually can protect your your customers, private your consumers, private identities, but still accomplish that. Join that Amy mentioned to be able to thio relate the cause and effect of these campaigns and really understand the signals. Um, that these data sets are trying to say about one another again without having to move data without having to reveal P. I We're seeing this happening now. This is this is the next big thing that we're gonna see explode over the next months and years to come. >>I totally agree. Massive changes coming in public policy in this area, and I wanted we only have a few minutes left. I wonder if for our audience members that you know, looking for some advice, what's the what's the one thing you'd recommend? They start doing differently or consider putting in place. That's going to set them up for success over the next decade. >>Yeah, that's a good question. Um, you know, I think e always say, you know, first harness all of your first party data across all touchpoints. Get that first party data in one place and working together Second back that data with trusted third parties and in mats, just in some ways to do that and then third, always with the customer first speak their language. Uh, where and when they want to be, uh, reached out thio on and use the information. You have to really create a better a better customer experience for your customers. >>Matt. What would you add to that? Bring us home if you would >>applications. Um, the idea that data can now be your data can now be pulled into your own business applications the same way that Netflix and Spotify are pulled into your consumer and lifestyle applications again without data moving these personalized applications experiences is what I encourage everyone to be thinking about from first principles. What would you do in your next app that you're gonna build? If you had all of your consumers, consumers had access to their data in the app and not having to think about things you know from scratch. Leverage the data cloud leverage these, you know, service providers like experience and build the applications of tomorrow. >>I'm super excited when I talked to practitioners like yourselves about the future of data Guys, Thanks so much for coming on. The Cube was really a pleasure having you and hope we can continue this conversation in the future. >>Thank you. >>All right. Thank you for watching. Keep it right there. We've got great content. Tons of content coming at the Snowflake Data Cloud Summit. This is Dave Volonte for the Cube. Keep it right there.

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE in ETR. This is Breaking Analysis with Dave Vellante. >> Snowflake is not going to grow into its valuation by stealing the croissant from the breakfast table of the on-prem data warehouse vendors. Look, even if snowflake got 100% of the data warehouse business, it wouldn't come close to justifying its market cap. Rather Snowflake has to create an entirely new market based on completely changing the way organizations think about monetizing data. Every organization I talk to says it wants to be, or many say they already are data-driven. why wouldn't you aspire to that goal? There's probably nothing more strategic than leveraging data to power your digital business and creating competitive advantage. But many businesses are failing, or I predict, will fail to create a true data-driven culture because they're relying on a flawed architectural model formed by decades of building centralized data platforms. Welcome everyone to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis, I want to share some new thoughts and fresh ETR data on how organizations can transform their businesses through data by reinventing their data architectures. And I want to share our thoughts on why we think Snowflake is currently in a very strong position to lead this effort. Now, on November 17th, theCUBE is hosting the Snowflake Data Cloud Summit. Snowflake's ascendancy and its blockbuster IPO has been widely covered by us and many others. Now, since Snowflake went public, we've been inundated with outreach from investors, customers, and competitors that wanted to either better understand the opportunities or explain why their approach is better or different. And in this segment, ahead of Snowflake's big event, we want to share some of what we learned and how we see it. Now, theCUBE is getting paid to host this event, so I need you to know that, and you draw your own conclusions from my remarks. But neither Snowflake nor any other sponsor of theCUBE or client of SiliconANGLE Media has editorial influence over Breaking Analysis. The opinions here are mine, and I would encourage you to read my ethics statement in this regard. I want to talk about the failed data model. The problem is complex, I'm not debating that. Organizations have to integrate data and platforms with existing operational systems, many of which were developed decades ago. And as a culture and a set of processes that have been built around these systems, and they've been hardened over the years. This chart here tries to depict the progression of the monolithic data source, which, for me, began in the 1980s when Decision Support Systems or DSS promised to solve our data problems. The data warehouse became very popular and data marts sprung up all over the place. This created more proprietary stovepipes with data locked inside. The Enron collapse led to Sarbanes-Oxley. Now, this tightened up reporting. The requirements associated with that, it breathed new life into the data warehouse model. But it remained expensive and cumbersome, I've talked about that a lot, like a snake swallowing a basketball. The 2010s ushered in the big data movement, and Data Lakes emerged. With a dupe, we saw the idea of no schema online, where you put structured and unstructured data into a repository, and figure it all out on the read. What emerged was a fairly complex data pipeline that involved ingesting, cleaning, processing, analyzing, preparing, and ultimately serving data to the lines of business. And this is where we are today with very hyper specialized roles around data engineering, data quality, data science. There's lots of batch of processing going on, and Spark has emerged to improve the complexity associated with MapReduce, and it definitely helped improve the situation. We're also seeing attempts to blend in real time stream processing with the emergence of tools like Kafka and others. But I'll argue that in a strange way, these innovations actually compound the problem. And I want to discuss that because what they do is they heighten the need for more specialization, more fragmentation, and more stovepipes within the data life cycle. Now, in reality, and it pains me to say this, it's the outcome of the big data movement, as we sit here in 2020, that we've created thousands of complicated science projects that have once again failed to live up to the promise of rapid cost-effective time to insights. So, what will the 2020s bring? What's the next silver bullet? You hear terms like the lakehouse, which Databricks is trying to popularize. And I'm going to talk today about data mesh. These are other efforts they look to modernize datalakes and sometimes merge the best of data warehouse and second-generation systems into a new paradigm, that might unify batch and stream frameworks. And this definitely addresses some of the gaps, but in our view, still suffers from some of the underlying problems of previous generation data architectures. In other words, if the next gen data architecture is incremental, centralized, rigid, and primarily focuses on making the technology to get data in and out of the pipeline work, we predict it's going to fail to live up to expectations again. Rather, what we're envisioning is an architecture based on the principles of distributed data, where domain knowledge is the primary target citizen, and data is not seen as a by-product, i.e, the exhaust of an operational system, but rather as a service that can be delivered in multiple forms and use cases across an ecosystem. This is why we often say the data is not the new oil. We don't like that phrase. A specific gallon of oil can either fuel my home or can lubricate my car engine, but it can't do both. Data does not follow the same laws of scarcity like natural resources. Again, what we're envisioning is a rethinking of the data pipeline and the associated cultures to put data needs of the domain owner at the core and provide automated, governed, and secure access to data as a service at scale. Now, how is this different? Let's take a look and unpack the data pipeline today and look deeper into the situation. You all know this picture that I'm showing. There's nothing really new here. The data comes from inside and outside the enterprise. It gets processed, cleanse or augmented so that it can be trusted and made useful. Nobody wants to use data that they can't trust. And then we can add machine intelligence and do more analysis, and finally deliver the data so that domain specific consumers can essentially build data products and services or reports and dashboards or content services, for instance, an insurance policy, a financial product, a loan, that these are packaged and made available for someone to make decisions on or to make a purchase. And all the metadata associated with this data is packaged along with the dataset. Now, we've broken down these steps into atomic components over time so we can optimize on each and make them as efficient as possible. And down below, you have these happy stick figures. Sometimes they're happy. But they're highly specialized individuals and they each do their job and they do it well to make sure that the data gets in, it gets processed and delivered in a timely manner. Now, while these individual pieces seemingly are autonomous and can be optimized and scaled, they're all encompassed within the centralized big data platform. And it's generally accepted that this platform is domain agnostic. Meaning the platform is the data owner, not the domain specific experts. Now there are a number of problems with this model. The first, while it's fine for organizations with smaller number of domains, organizations with a large number of data sources and complex domain structures, they struggle to create a common data parlance, for example, in a data culture. Another problem is that, as the number of data sources grows, organizing and harmonizing them in a centralized platform becomes increasingly difficult, because the context of the domain and the line of business gets lost. Moreover, as ecosystems grow and you add more data, the processes associated with the centralized platform tend to get further genericized. They again lose that domain specific context. Wait (chuckling), there are more problems. Now, while in theory organizations are optimizing on the piece parts of the pipeline, the reality is, as the domain requires a change, for example, a new data source or an ecosystem partnership requires a change in access or processes that can benefit a domain consumer, the reality is the change is subservient to the dependencies and the need to synchronize across these discrete parts of the pipeline or actually, orthogonal to each of those parts. In other words, in actuality, the monolithic data platform itself remains the most granular part of the system. Now, when I complain about this faulty structure, some folks tell me this problem has been solved. That there are services that allow new data sources to really easily be added. A good example of this is Databricks Ingest, which is, it's an auto loader. And what it does is it simplifies the ingestion into the company's Delta Lake offering. And rather than centralizing in a data warehouse, which struggles to efficiently allow things like Machine Learning frameworks to be incorporated, this feature allows you to put all the data into a centralized datalake. More so the argument goes, that the problem that I see with this, is while the approach does definitely minimizes the complexities of adding new data sources, it still relies on this linear end-to-end process that slows down the introduction of data sources from the domain consumer beside of the pipeline. In other words, the domain experts still has to elbow her way into the front of the line or the pipeline, in this case, to get stuff done. And finally, the way we are organizing teams is a point of contention, and I believe is going to continue to cause problems down the road. Specifically, we've again, we've optimized on technology expertise, where for example, data engineers, well, really good at what they do, they're often removed from the operations of the business. Essentially, we created more silos and organized around technical expertise versus domain knowledge. As an example, a data team has to work with data that is delivered with very little domain specificity, and serves a variety of highly specialized consumption use cases. All right. I want to step back for a minute and talk about some of the problems that people bring up with Snowflake and then I'll relate it back to the basic premise here. As I said earlier, we've been hammered by dozens and dozens of data points, opinions, criticisms of Snowflake. And I'll share a few here. But I'll post a deeper technical analysis from a software engineer that I found to be fairly balanced. There's five Snowflake criticisms that I'll highlight. And there are many more, but here are some that I want to call out. Price transparency. I've had more than a few customers telling me they chose an alternative database because of the unpredictable nature of Snowflake's pricing model. Snowflake, as you probably know, prices based on consumption, just like AWS and other cloud providers. So just like AWS, for example, the bill at the end of the month is sometimes unpredictable. Is this a problem? Yes. But like AWS, I would say, "Kill me with that problem." Look, if users are creating value by using Snowflake, then that's good for the business. But clearly this is a sore point for some users, especially for procurement and finance, which don't like unpredictability. And Snowflake needs to do a better job communicating and managing this issue with tooling that can predict and help better manage costs. Next, workload manage or lack thereof. Look, if you want to isolate higher performance workloads with Snowflake, you just spin up a separate virtual warehouse. It's kind of a brute force approach. It works generally, but it will add expense. I'm kind of reminded of Pure Storage and its approach to storage management. The engineers at Pure, they always design for simplicity, and this is the approach that Snowflake is taking. Usually, Pure and Snowflake, as I have discussed in a moment, is Pure's ascendancy was really based largely on stealing share from Legacy EMC systems. Snowflake, in my view, has a much, much larger incremental market opportunity. Next is caching architecture. You hear this a lot. At the end of the day, Snowflake is based on a caching architecture. And a caching architecture has to be working for some time to optimize performance. Caches work well when the size of the working set is small. Caches generally don't work well when the working set is very, very large. In general, transactional databases have pretty small datasets. And in general, analytics datasets are potentially much larger. Is it Snowflake in the analytics business? Yes. But the good thing that Snowflake has done is they've enabled data sharing, and it's caching architecture serves its customers well because it allows domain experts, you're going to hear this a lot from me today, to isolate and analyze problems or go after opportunities based on tactical needs. That said, very big queries across whole datasets or badly written queries that scan the entire database are not the sweet spot for Snowflake. Another good example would be if you're doing a large audit and you need to analyze a huge, huge dataset. Snowflake's probably not the best solution. Complex joins, you hear this a lot. The working set of complex joins, by definition, are larger. So, see my previous explanation. Read only. Snowflake is pretty much optimized for read only data. Maybe stateless data is a better way of thinking about this. Heavily right intensive workloads are not the wheelhouse of Snowflake. So where this is maybe an issue is real-time decision-making and AI influencing. A number of times, Snowflake, I've talked about this, they might be able to develop products or acquire technology to address this opportunity. Now, I want to explain. These issues would be problematic if Snowflake were just a data warehouse vendor. If that were the case, this company, in my opinion, would hit a wall just like the NPP vendors that proceeded them by building a better mouse trap for certain use cases hit a wall. Rather, my promise in this episode is that the future of data architectures will be really to move away from large centralized warehouses or datalake models to a highly distributed data sharing system that puts power in the hands of domain experts at the line of business. Snowflake is less computationally efficient and less optimized for classic data warehouse work. But it's designed to serve the domain user much more effectively in our view. We believe that Snowflake is optimizing for business effectiveness, essentially. And as I said before, the company can probably do a better job at keeping passionate end users from breaking the bank. But as long as these end users are making money for their companies, I don't think this is going to be a problem. Let's look at the attributes of what we're proposing around this new architecture. We believe we'll see the emergence of a total flip of the centralized and monolithic big data systems that we've known for decades. In this architecture, data is owned by domain-specific business leaders, not technologists. Today, it's not much different in most organizations than it was 20 years ago. If I want to create something of value that requires data, I need to cajole, beg or bribe the technology and the data team to accommodate. The data consumers are subservient to the data pipeline. Whereas in the future, we see the pipeline as a second class citizen, with a domain expert is elevated. In other words, getting the technology and the components of the pipeline to be more efficient is not the key outcome. Rather, the time it takes to envision, create, and monetize a data service is the primary measure. The data teams are cross-functional and live inside the domain versus today's structure where the data team is largely disconnected from the domain consumer. Data in this model, as I said, is not the exhaust coming out of an operational system or an external source that is treated as generic and stuffed into a big data platform. Rather, it's a key ingredient of a service that is domain-driven and monetizable. And the target system is not a warehouse or a lake. It's a collection of connected domain-specific datasets that live in a global mesh. What is a distributed global data mesh? A data mesh is a decentralized architecture that is domain aware. The datasets in the system are purposely designed to support a data service or data product, if you prefer. The ownership of the data resides with the domain experts because they have the most detailed knowledge of the data requirement and its end use. Data in this global mesh is governed and secured, and every user in the mesh can have access to any dataset as long as it's governed according to the edicts of the organization. Now, in this model, the domain expert has access to a self-service and obstructed infrastructure layer that is supported by a cross-functional technology team. Again, the primary measure of success is the time it takes to conceive and deliver a data service that could be monetized. Now, by monetize, we mean a data product or data service that it either cuts cost, it drives revenue, it saves lives, whatever the mission is of the organization. The power of this model is it accelerates the creation of value by putting authority in the hands of those individuals who are closest to the customer and have the most intimate knowledge of how to monetize data. It reduces the diseconomies at scale of having a centralized or a monolithic data architecture. And it scales much better than legacy approaches because the atomic unit is a data domain, not a monolithic warehouse or a lake. Zhamak Dehghani is a software engineer who is attempting to popularize the concept of a global mesh. Her work is outstanding, and it's strengthened our belief that practitioners see this the same way that we do. And to paraphrase her view, "A domain centric system must be secure and governed with standard policies across domains." It has to be trusted. As I said, nobody's going to use data they don't trust. It's got to be discoverable via a data catalog with rich metadata. The data sets have to be self-describing and designed for self-service. Accessibility for all users is crucial as is interoperability, without which distributed systems, as we know, fail. So what does this all have to do with Snowflake? As I said, Snowflake is not just a data warehouse. In our view, it's always had the potential to be more. Our assessment is that attacking the data warehouse use cases, it gave Snowflake a straightforward easy-to-understand narrative that allowed it to get a foothold in the market. Data warehouses are notoriously expensive, cumbersome, and resource intensive, but they're a critical aspect to reporting and analytics. So it was logical for Snowflake to target on-premise legacy data warehouses and their smaller cousins, the datalakes, as early use cases. By putting forth and demonstrating a simple data warehouse alternative that can be spun up quickly, Snowflake was able to gain traction, demonstrate repeatability, and attract the capital necessary to scale to its vision. This chart shows the three layers of Snowflake's architecture that have been well-documented. The separation of compute and storage, and the outer layer of cloud services. But I want to call your attention to the bottom part of the chart, the so-called Cloud Agnostic Layer that Snowflake introduced in 2018. This layer is somewhat misunderstood. Not only did Snowflake make its Cloud-native database compatible to run on AWS than Azure in the 2020 GCP, what Snowflake has done is to obstruct cloud infrastructure complexity and create what it calls the data cloud. What's the data cloud? We don't believe the data cloud is just a marketing term that doesn't have any substance. Just as SAS is Simplified Application Software and iOS made it possible to eliminate the value drain associated with provisioning infrastructure, a data cloud, in concept, can simplify data access, and break down fragmentation and enable shared data across the globe. Snowflake, they have a first mover advantage in this space, and we see a number of fundamental aspects that comprise a data cloud. First, massive scale with virtually unlimited compute and storage resource that are enabled by the public cloud. We talk about this a lot. Second is a data or database architecture that's built to take advantage of native public cloud services. This is why Frank Slootman says, "We've burned the boats. We're not ever doing on-prem. We're all in on cloud and cloud native." Third is an obstruction layer that hides the complexity of infrastructure. and fourth is a governed and secured shared access system where any user in the system, if allowed, can get access to any data in the cloud. So a key enabler of the data cloud is this thing called the global data mesh. Now, earlier this year, Snowflake introduced its global data mesh. Over the course of its recent history, Snowflake has been building out its data cloud by creating data regions, strategically tapping key locations of AWS regions and then adding Azure and GCP. The complexity of the underlying cloud infrastructure has been stripped away to enable self-service, and any Snowflake user becomes part of this global mesh, independent of the cloud that they're on. Okay. So now, let's go back to what we were talking about earlier. Users in this mesh will be our domain owners. They're building monetizable services and products around data. They're most likely dealing with relatively small read only datasets. They can adjust data from any source very easily and quickly set up security and governance to enable data sharing across different parts of an organization, or, very importantly, an ecosystem. Access control and governance is automated. The data sets are addressable. The data owners have clearly defined missions and they own the data through the life cycle. Data that is specific and purposely shaped for their missions. Now, you're probably asking, "What happens to the technical team and the underlying infrastructure and the cluster it's in? How do I get the compute close to the data? And what about data sovereignty and the physical storage later, and the costs?" All these are good questions, and I'm not saying these are trivial. But the answer is these are implementation details that are pushed to a self-service layer managed by a group of engineers that serves the data owners. And as long as the domain expert/data owner is driving monetization, this piece of the puzzle becomes self-funding. As I said before, Snowflake has to help these users to optimize their spend with predictive tooling that aligns spend with value and shows ROI. While there may not be a strong motivation for Snowflake to do this, my belief is that they'd better get good at it or someone else will do it for them and steal their ideas. All right. Let me end with some ETR data to show you just how Snowflake is getting a foothold on the market. Followers of this program know that ETR uses a consistent methodology to go to its practitioner base, its buyer base each quarter and ask them a series of questions. They focus on the areas that the technology buyer is most familiar with, and they ask a series of questions to determine the spending momentum around a company within a specific domain. This chart shows one of my favorite examples. It shows data from the October ETR survey of 1,438 respondents. And it isolates on the data warehouse and database sector. I know I just got through telling you that the world is going to change and Snowflake's not a data warehouse vendor, but there's no construct today in the ETR dataset to cut a data cloud or globally distributed data mesh. So you're going to have to deal with this. What this chart shows is net score in the y-axis. That's a measure of spending velocity, and it's calculated by asking customers, "Are you spending more or less on a particular platform?" And then subtracting the lesses from the mores. It's more granular than that, but that's the basic concept. Now, on the x-axis is market share, which is ETR's measure of pervasiveness in the survey. You can see superimposed in the upper right-hand corner, a table that shows the net score and the shared N for each company. Now, shared N is the number of mentions in the dataset within, in this case, the data warehousing sector. Snowflake, once again, leads all players with a 75% net score. This is a very elevated number and is higher than that of all other players, including the big cloud companies. Now, we've been tracking this for a while, and Snowflake is holding firm on both dimensions. When Snowflake first hit the dataset, it was in the single digits along the horizontal axis and continues to creep to the right as it adds more customers. Now, here's another chart. I call it the wheel chart that breaks down the components of Snowflake's net score or spending momentum. The lime green is new adoption, the forest green is customers spending more than 5%, the gray is flat spend, the pink is declining by more than 5%, and the bright red is retiring the platform. So you can see the trend. It's all momentum for this company. Now, what Snowflake has done is they grabbed a hold of the market by simplifying data warehouse. But the strategic aspect of that is that it enables the data cloud leveraging the global mesh concept. And the company has introduced a data marketplace to facilitate data sharing across ecosystems. This is all about network effects. In the mid to late 1990s, as the internet was being built out, I worked at IDG with Bob Metcalfe, who was the publisher of InfoWorld. During that time, we'd go on speaking tours all over the world, and I would listen very carefully as he applied Metcalfe's law to the internet. Metcalfe's law states that the value of the network is proportional to the square of the number of connected nodes or users on that system. Said another way, while the cost of adding new nodes to a network scales linearly, the consequent value scores scales exponentially. Now, apply that to the data cloud. The marginal cost of adding a user is negligible, practically zero, but the value of being able to access any dataset in the cloud... Well, let me just say this. There's no limitation to the magnitude of the market. My prediction is that this idea of a global mesh will completely change the way leading companies structure their businesses and, particularly, their data architectures. It will be the technologists that serve domain specialists as it should be. Okay. Well, what do you think? DM me @dvellante or email me at david.vellante@siliconangle.com or comment on my LinkedIn? Remember, these episodes are all available as podcasts, so please subscribe wherever you listen. I publish weekly on wikibon.com and siliconangle.com, and don't forget to check out etr.plus for all the survey analysis. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching. Be well, and we'll see you next time. (upbeat music)

>>Hello, everyone. And welcome back to our wall to wall coverage of the data Cloud Summit. This is Dave a lot. And we're seeing the emergence of a next generation workload in the cloud were more facile access and governed. Sharing of data is accelerating. Time to insights and action. All right, allow me to introduce our next guest. Amy Irwin is here. She's the vice president of strategy for experience. And Matt Glickman is VP customer product strategy it snowflake with an emphasis on financial services. Folks, welcome to the Cube. Thanks so much for coming on. >>Thanks for >>having us >>nice to be here. Hey, >>So, Amy, I mean, obviously 2020 has been pretty unique and crazy and challenging time for a lot of people. I don't know why I've been checking my credit score a lot more for some reason. On the app I love the app I got hacked. I had a lock it the other day I locked my credit. Somebody tried to dio on and it worked. I was so happy. So thank you for that. But so we know experience, but there's a ton of data behind what you do. I wonder if you could share kind of where you sit in the data space and how you've seen organizations leverage data up to this point. And really, if you could address maybe some of the changes that you're seeing as a result of the pandemic, that would be great. >>Sure, sure. Well, Azaz, you mentioned experience Eyes best known as a credit bureau. Uh, I work in our marketing services business unit, and what we do is we really help brands leverage the power of data and technology to make the right marketing decisions and better understand and connect with consumers. Eso We offer marketers products around data identity activation measurement. We have a consumer view data file that's based on offline P I and contains demographic interest, transaction data and other attributes on about 300 million people in the U. S. Uh, and on the identity side, we've always been known for our safe haven or privacy friendly matching that allows marketers to connect their first party data to experience or other third parties. Uh, but in today's world, with the growth and importance of digital advertising and consumer behavior shifting to digital, uh, experience also is working to connect that offline data to the digital world for a complete view of the customer you mentioned co vid, um, we actually, we start of many different verticals. And what we're seeing from our clients during co vid is that there's a bearing impact of the pandemic. The common theme is that those that have successfully pivoted their businesses to digital are doing much better. Uh, as we all know, Kobe accelerated very strong trends to digital both in the commerce and immediately eating habits. We work with a lot of retailers. Retail is a tale of two cities with big box and grocery growing and apparel retail really struggling. We've helped our clients leveraging our data to better understand the shifts in these consumer behaviors and better segment their customers during this really challenging time. Eso think about there's there's a group of customers that it's still staying home that is sheltered in place. There's a group of customers starting that significantly varied their consumer behavior, but it's starting to venture out a little. And then there's a group of customers that's doing largely what they did before in a somewhat modified fashion. So we're helping our clients segment those customers into groups to try and understand the right messaging and right offers for each of those groups. And we're also helping them with at risk. Audi's is S O. That's more on the financial side. Which of your customers are really struggling due to the pandemic. And how do you respond? >>So it's awesome. Thank you. You know it Zafon e I mean somebody. I saw Twitter poll today asking if we measure our screen time and I said, Oh my no eso Matt, let me ask you. You spend a ton of time and financial services. You really kind of cut your teeth there, and it's always been very data oriented. You've seen a lot of changes tell us about how your customers are bringing together data, the skills that people obviously a big part of the equation and applications to really put data at the center of their universe. What's new and different that these companies are getting out of the investments in data and skills. >>That's a great question. Um, the acceleration that Amy mentioned Israel, Um, we're seeing a particularly this year, but I think even in the past few years, the reluctance of customers to embrace. The cloud is behind us. And now there's this massive acceleration to be able to go faster on, and in some ways the new entrance into this category have an advantage versus, you know, the companies that have been in the space, whether it's financial services or beyond. Um, and in a lot of ways they are are seeing the cloud and services like snowflakes as a way toe not only catch up but leapfrog your competitors and really deliver a differentiated experience to your customers to your business, internally or externally. Um, and this past, you know, however long this crisis has been going on, has really only accelerated that, because now there's a new demand. Understand your customer better your your business better with with your traditional data sources and also new alternative data sources, Um, and also be able to take a pulse. One of things that we learned which was you know, I opening experience was as the crisis unfolded, one of our data partners decided to take the data sets about where the cases where were happening from the Johns Hopkins and World Health Organization and put that on our platform and it became a runaway hit. Where now, with thousands of our customers overnight, we're using this data to understand how their business was doing versus how the crisis was unfolding in real time. On this has been a game changer, and I think it's only it's only scratching the surface of what now the world will be able to do when data is really at their fingertips. You're not hindered by your legacy platforms. >>I wrote about that back in the early days of the pandemic when you guys did that and talked about some of the changes that you guys enabled. And you know you're right about Cloud. I mean, financial services. Cloud used to be an evil word, and now it's almost become a mandate. Amy, I >>wonder if you >>could tell us a little bit more about what? What you know your customers they're having to work through in order to achieve some of these outcomes. I mean, I'm interested in the starting point. I've been talking a lot and writing a lot on talking to practitioners about what I call the data lifecycle. Sometimes people call it the data pipeline. It's it's a complicated matter, but those customers and companies that can put data at the center and really treat that pipeline is, you know, the heart of their organization, if you will, Really succeeding. What are you seeing and what really is the starting point there? >>Yes, yes, that's a good question. And as you mentioned, first party, I mean, we start with first party data. Right? First party data is critical to understanding consumers on been in different verticals, different companies. Different brands have varying levels of first party data. So retailers gonna have a lot more first party data financial services company, then say an auto manufacturer. Uh, while many marketers have that first party data to really have a 3 60 view of the customer, they need third party data as well. And that's where experience comes in. We help brands connect those disparate data sets both 1st and 3rd party baked data to better understand consumers and create a single customer view, which has a number of applications. I think the last that I heard was that there's about eight devices on average per person. I always joke that we're gonna have these enormous. I mean, that that number is growing we're gonna have these enormous charging stations in our house, and I think we're because all the different devices and way seamlessly move from device to device along our customer journey. And, um, if the brand doesn't understand who we are, it's much harder for the brand to connect with consumers and create a positive customer experience and way site that about 95% of companies are actually that they are looking to achieve that single customer view. They recognize, um, that they need that. And they've aligned various teams from e commerce to marketing to sales so at a minimum in just their first party data, and then connect that data to better understand, uh, consumers. So consumers can interact with the brand through website and mobile app in store visits, um, by the phone TV ad, etcetera. And a brand needs to use all of those touchpoints often collected by different parts of the organization and then adding that third party data to really understand the consumers in terms of specific use cases, Um, there's there's about three that come to mind. So there's first. There's relevant advertising and reaching the right customer. There's measurement s or being able to evaluate your advertising efforts. Uh, if you see an ad on if I see it out of my mobile and then I by by visiting a desktop website, understanding or I get a direct mail piece understanding that those connect those interactions are all connected to the same person is critical for measurement. And then there's, uh, there's personalization, um, which includes improved customer experience amongst your own, um, touch points with that consumer Parsons marketing communication and then, of course, um, analytics. So those are the use cases we're seeing? Great. >>Thank you, Amy. I'm at you Can't really talk about data without talking about, >>you know, >>governance and and and compliance. And I remember back in 2006 when the Federal Rules of Civil Procedure went in, it was easy. The lawyers just said, No, nobody can have access, but that's changed. One of things I like about what snowflakes doing with the data cloud is it's really about democratizing access, but doing so in a way that gives people confidence that they only have access to the right data. So maybe you could talk a little bit about how you're thinking about this topic what you're doing to help customers navigate, which has traditionally been such a really challenging problem. >>No, it's another great question. Um, this is where I think the major disruption is happening. Um, and what Amy described being able to join together 1st and 3rd party data sets. Um, being able to do this was always a challenge because data had to be moved around, had to ship my first party data to the other side. The third party data had to be shipped to me. And being able to join those data sets together, um was problematic at best. And now, with the focus on privacy and protecting P, I, um, this is this is something that has to change. And the good news is with the data cloud data does not have to move. Data can stay where it belongs experience and keep its data experience. Customers can hold on to their data. Yet the data can be joined together on this universal global platform that we call the data cloud. On top of that, and particularly with the regulations that are coming out that are going to prevent data from being collected on either a mobile device or in wet warn as cookies and Web browsers. New approaches and we're seeing this a lot in our space, both in financials and in media is to set up these data clean rooms where both sides can give access to one another but not have to reveal any P i i to do that joint. Um, this is gonna be huge right now. You actually can protect your your customers, private your consumers, private identities, but still accomplish that. Join that Amy mentioned to be able to thio, relate the cause and effect of these campaigns and really understand the signals that these data sets are trying to say about one another again without having to move data without having to reveal P. I We're seeing this happening now. This is this is the next big thing that we're gonna see explode over the next months and years to come. >>I totally agree massive changes coming in public policy in this area, and I wanted we only have a few minutes left. I wonder if for our audience members that you know, looking for some advice, what's the what's the one thing you'd recommend? They start doing differently or consider putting in place That's going to set them up for success over the next decade. >>Yeah, that's a good question. Um, you know, I think e always say, you know, first harness all of your first party data across all touchpoints. Get that first party data in one place and working together psychic back that data with trusted third parties and mats, just in some ways to do that and then third, always with the customer first speak their language, uh, where and when they want to be, uh, reached out thio on and use the information. You have to really create a better a better customer experience for your customers. >>Matt. What would you add to that? Bring us home if you would >>applications. Um, the idea that data can now be your data can now be pulled into your own business applications the same way that Netflix and Spotify are pulled into your consumer and lifestyle applications again without data moving these personalized applications experiences is what I encourage everyone to be thinking about from first principles. What would you do in your next app that you're going to build? If you had all of your consumers. Consumers had access to their data in the APP and not having to think about things, you know, from scratch. Leverage the data cloud leverage these, you know, service providers like experience and build the applications of tomorrow. >>I'm super excited when I talked to practitioners like yourselves about the future of data Guys. Thanks so much for coming on. The Cube was really a pleasure having you and hope we can continue this conversation in the future. >>Thank you. >>Anything. >>All right. Thank you for watching. Keep it right there. We've got great content. Tons of content coming at the Snowflake Data Cloud Summit. This is Dave Volonte for the Cube. Keep it right there.

>> Hello everyone, welcome back to theCUBE'S wall to wall coverage of the Snowflake Data Cloud Summit. My name is Dave Vellante and with me are two world-class technologists, visionaries, and entrepreneurs. Benoit Dageville is the, he co-founded Snowflake. And he's now the president of the Product division and Florian Douetteau is the co-founder and CEO of Dataiku. Gentlemen, welcome to theCUBE, two first timers, love it. >> Great time to be here. >> Now Florian, you and Benoit, you have a number of customers in common. And I've said many times on theCUBE that, the first era of cloud was really about infrastructure, making it more agile taking out costs. And the next generation of innovation is really coming from the application of machine intelligence to data with the cloud, is really the scale platform. So is that premise relevant to you, do you buy that? And why do you think Snowflake and Dataiku make a good match for customers? >> I think that because it's our values that align. When it gets all about actually today, and knowing complexity per customer, so you close the gap or we need to commoditize the access to data, the access to technology, it's not only about data, data is important, but it's also about the impacts of data. How can you make the best out of data as fast as possible, as easily as possible within an organization? And another value is about just the openness of the platform, building a future together. I think a platform that is not just about the platform but also for the ecosystem of partners around it, bringing the little bit of accessibility and flexibility, you need for the 10 years of that. >> Yes, so that's key, but it's not just data. It's turning data into insights. Now Benoit, you came out of the world of very powerful, but highly complex databases. And we all know that, you and the Snowflake team, you get very high marks for really radically simplifying customers' lives. But can you talk specifically about the types of challenges that your customers are using Snowflake to solve? >> Yeah, so really the challenge before Snowflake, I would say, was really to put all the data, in one place and run all the computes, all the workloads that you wanted to run, against that data. And of course, existing legacy platforms were not able to support that level of concurrency, many workload. We talk about machine learning, data science, data engineering, data warehouse, big data workloads, all running in one place, didn't make sense at all. And therefore, what customers did, is to create silos, silos of data everywhere, with different systems having a subset of the data. And of course now you cannot analyze this data in one place. So Snowflake, we really solved that problem by creating a single architecture where you can put all the data in the cloud. So it's a really cloud native. We really thought about how to solve that problem, how to create leverage cloud and the elasticity of cloud to really put all the data in one place. But at the same time, not run all workload at the same place. So each workload that runs in Snowflake at least dedicate compute resources to run. And that makes it very agile, right. Florian talked about data scientist having to run analysis. So they need a lot of compute resources, but only for few hours and with Snowflake, they can run these new workload, add this workload to the system, get the compute resources that they need to run this workload. And then when it's over, they can shut down their system. It will automatically shut down. Therefore they would not pay for the resources that they don't choose. So it's a very agile system, where you can do these analysis when you need, and you have all the power to run all these workload at the same time. >> Well, it's profound what you guys built. To me, I mean, because everybody's trying to copy it now. It's like, I remember the notion of bringing compute to the data in the Hadoop days. And I think that, as I say, everybody is sort of following your suit now or trying to. Florian, I got to say, the first data scientist I ever interviewed on theCUBE was the amazing Hilary Mason, right after she started at Bitly. And she made data science sounds so compelling, but data science is hard. So same question for you. What do you see is the biggest challenges for customers that they're facing with data science? >> The biggest challenge from my perspective is that once you solve the issue of the data silo with Snowflake, you don't want to bring another silo, which would be a silo of skills. And essentially, thanks to that talent gap between the talent and labor of the markets, or how it is to actually find, recruit and train data scientists and what needs to be done. And so you need actually to simplify the access to technology such as every organization can make it, whatever the talents by bridging that gap. And to get there, there is a need of actually breaking up the silos. I think a collaborative approach, where technologies and business work together and actually all put some of their ends into those data projects together. >> Yeah, it makes sense. So Florian, Let's stay with you for a minute, if I can. Your observation spaces, is pretty, pretty global. And so, you have a unique perspective on how companies around the world might be using data and data science. Are you seeing any trends, maybe differences between regions or maybe within different industries? What are you seeing? >> Yep. Yeah, definitely, I do see trends that are not geographic that much, but much more in terms of maturity of certain industries and certain sectors, which are that certain industries invested a lot in terms of data, data access, ability to store data as well as few years and know each level of maturity where they can invest more and get to the next steps. And it's really reliant to reach out to certain details, certain organization, actually to have built this longterm data strategy a few years ago, and no stocks ripping off the benefits. >> You know, a decade ago, Florian, Hal Varian famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of changed that to data scientists. And then everybody, all the statisticians became data scientists and they got a raise. But data science requires more than just statistics acumen. What skills do you see is critical for the next generation of data science? >> Yeah, it's a good question because I think the first generation of data scientists became better scientists because they could learn some Python quickly and be flexible. And I think that skills of the next generation of data scientists will definitely be different. It will be first about being able to speak the language of the business, meaning all you translate data insight, predictive modeling, all of this into actionable insights or business impact. And it will be about who you collaborate with the rest of the business. It's not just how fast you can build something, how fast you can do a notebook in Python or do quantity models of some sorts. It's about how you actually build this bridge with the business. And obviously those things are important, but we also must be cognizant of the fact that technology will evolve in the future. There will be new tools in technologies, and they will still need to get this level of flexibility and get to understand quickly what are the next tools, they need to use or new languages or whatever to get there. >> Thank you for that. Benoit, let's come back to you. This year has been tumultuous to say the least for everyone, but it's a good time to be in tech, ironically. And if you're in cloud, it's even better. But you look at Snowflake and Dataiku, you guys had done well, despite the economic uncertainty and the challenges of the pandemic. As you look back on 2020, what are you thinking? What are you telling people as we head into next year? >> Yeah, I think it's very interesting, right. We, this crisis has told us that the world really can change from one day to the next. And this has dramatic and profound aspects. For example, companies all of a sudden, saw their revenue line dropping and they had to do less with data. And some of the companies was the reverse, right? All of a sudden, they were online like Instacart, for example, and their business completely change from one day to the other. So this agility of adjusting the resources that you have to do the task, a need that can change, using solution like Snowflake, really helps that. And we saw both in our customers. Some customers from one day to the next, were growing like big time, because they benefited from COVID and their business benefited, but also, as you know, had to drop and what is nice with cloud, it allows to adjust compute resources to your business needs and really address it in-house. The other aspect is understanding what is happening, right? You need to analyze. So we saw all our customers basically wanted to understand, what is it going to be the impact on my business? How can I adapt? How can I adjust? And for that, they needed to analyze data. And of course, a lot of data, which are not necessarily data about their business, but also data from the outside. For example, COVID data. Where is the state, what is the impact, geographic impact on COVID all the time. And access to this data is critical. So this is the promise of the data cloud, right? Having one single place where you can put all the data of the world. So, our customers all of a sudden, started to consume the COVID data from our data marketplace. And we have the unit already thousands of customers looking at this data, analyzing this data to make good decisions. So this agility and this adapting from one hour to the next is really critical and that goes with data, with cloud, more interesting resources and that's doesn't exist on premise. So, indeed I think the lesson learned is, we are living in a world which is changing all the time, and we have to understand it. We have to adjust and that's why cloud, some way is great. >> Excellent, thank you. You know, in theCUBE, we like to talk about disruption, of course, who doesn't. And also, I mean, you look at AI and the impact that it's beginning to have and kind of pre-COVID, you look at some of the industries that were getting disrupted by, everybody talks about digital transformation and you had on the one end of the spectrum, industries like publishing, which are highly disrupted or taxis, and you can say, "Okay well, that's Bits versus Adam, the old Negroponte thing." But then the flip side of this, it says, "Look at financial services that hadn't been dramatically disrupted, certainly healthcare, which is right for disruption, defense." So the more the number of industries that really hadn't leaned into digital transformation, if it ain't broke, don't fix it. Not on my watch. There was this complacency. And then of course COVID broke everything. So Florian, I wonder if you could comment, what industry or industries do you think are going to be most impacted by data science and what I call machine intelligence or AI in the coming years and decades? >> Honestly, I think it's all of them, or at least most of them. Because for some industries, the impact is very visible because we are talking about brand new products, drones, flying cars, or whatever is that are very visible for us. But for others, we are talking about spectrum changes in the way you operate as an organization. Even if financial industry itself doesn't seem to be so impacted when you look at it from the consumer side or the outside. In fact internally, it's probably impacted just because of the way you use data to develop for flexibility you need, is there kind of a cost gain you can get by leveraging the latest technologies, is just enormous. And so it will, actually comes from the industry, that also. And overall, I think that 2020 is a year where, from the perspective of AI and analytics, we understood this idea of maturity and resilience. Maturity, meaning that when you've got a crisis, you actually need data and AI more than before, you need to actually call the people from data in the room to take better decisions and look forward and not backward. And I think that's a very important learning from 2020 that will tell things about 2021. And resilience, it's like, yeah, data analytics today is a function consuming every industries, and is so important that it's something that needs to work. So the infrastructure needs to work, the infrastructure needs to be super resilient. So probably not on trend and not fully on trend, at some point and the kind of residence where you need to be able to plan for literally anything. like no hypothesis in terms of behaviors can be taken for granted. And that's something that is new and which is just signaling that we are just getting into a next step for all data analytics. >> I wonder Benoit, if you have anything to add to that, I mean, I often wonder, you know, when are machines going to be able to make better diagnoses than doctors, some people say already. Will the financial services, traditional banks lose control of payment systems? You know, what's going to happen to big retail stores? I mean, may be bring us home with maybe some of your final thoughts. >> Yeah, I would say, I don't see that as a negative, right? The human being will always be involved very closely, but then the machine and the data can really help, see correlation in the data that would be impossible for human being alone to discover. So, I think it's going to be a compliment, not a replacement and everything that has made us faster, doesn't mean that we have less work to do. It means that we can do more. And we have so much to do. That I would not be worried about the effect of being more efficient and better at our work. And indeed, I fundamentally think that, data, processing of images and doing AI on these images and discovering patterns and potentially flagging disease, way earlier than it was possible, it is going to have a huge impact in health care. And as Florian was saying, every industry is going to be impacted by that technology. So, yeah, I'm very optimistic. >> Great, Guys, I wish we had more time. We got to leave it there but so thanks so much for coming on theCUBE. It was really a pleasure having you. >> [Benoit & Florian] Thank you. >> You're welcome but keep it right there, everybody. We'll back with our next guest, right after this short break. You're watching theCUBE.