>>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.

>> 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 covering the Upgrade 2020, the NTT Research Summit presented by NTT Research. >> Hi, I'm Stu Miniman, and this is the Cube's coverage of Upgrade 2020 the Global Research Summit for NTT and always happy when we get to talk about digital transformation. Happy to welcome to the program, first time guest on the program, Eric Clark. He is the Chief Digital Officer with NTT data. Eric, thanks so much for joining us. >> Thank you, glad to be here. >> All right so Eric, let's start, you know, CDOs, first of all, there's lots of CDOs. We've done lots of events with the Chief Data Officers, which I'm sure we'll talk a little bit about data, but the digital officers, of course digital so important in general and even more so in 2020. But let's understand your role as Chief Digital Officer. What's your charter? Where you sit in the work? What are you responsible for? >> Yeah, definitely, and you know, it's a good question. I often start conversations with our customers by talking about exactly that, because Chief Digital Officer means something different to different companies. So for us, it's primarily my market facing. And what that means is I spend most of my time looking at research, looking at R&D, looking at what our competitors are doing in the market and looking at where trends are going to make sure that we have the right offerings and capabilities to bring to our customers, to make sure that they will remain competitive in their markets. >> That's great, you know, we've been talking for years about the digital transformations that companies have been going through. One of our definitions has been, if you're not at the end of it, more data-driven, you probably haven't done the right thing. But Eric, this year with 2020, you know, anecdotally, we talked to a lot of customers and obviously there's certain initiatives that get frozen or will take a little bit longer, but those digital initiatives, which are supposed to rely on data and help us move fast and be more agile, seem to be at the top of the list and are accelerating because if I can't respond to the daily and weekly changes that have been great in 2020, I might have a tough time surviving. So, what are you seeing? How does that live in your world? >> Yeah, you're exactly right. And that's what we're seeing from our client base as well. So early on in the pandemic, there was a lot of freeze. You know, hold everything, stop, stop spending, and let's figure out where we are and where this is going. But very quickly that turned to, we've got to react. We're going to be living with this for awhile. And we can't afford to sit back and wait and see where it goes. We've got to react and we've got to direct our future. And very often the way that comes out is with digital. So, customers are looking for opportunities to leverage digital, to grow revenue, to improve customer engagement and to drive more of their revenue through digital channels. >> Interesting, but one thing I didn't here in there, but I'm sure is part if it, what about the employees themselves? One of the big things of course, is that we've made this wonderful corporate environment, you've got the great internet there and now way everybody's at home and scrambling as to what they do. So how about the kind of the EX to go along with the CX? >> Yeah, exactly, and that was actually one of the first places that we focused as a company, because we do a lot of what we refer to as workplace services. So making sure that our customers, employees have the tools they need to do their job successfully. So immediately when offices started closing and people started going home, our big challenge was let's make sure that our customers can connect from anywhere, from wherever they need to be working from and have access to the applications and the tools and the products that they need to perform their jobs remotely. And that's really turned into a significant business of its own, really addressing those needs, not only for our customers, but also for our employee base. We have 50,000 people that we sent home, more than 90% of overnight. And many of these are our employees that are interacting with our customer base on a daily basis. So we had to make sure not only that they had connected but they had to be secure. So it was a very big switch and I think I personally was really impressed not only with what we did, but what we saw the industry do, to make that transition very safely and seamlessly. >> Eric I'd love you to expand a little bit on that, You know, which pieces of that full solution that is NTT offering and how do you and your partners help your customers through those rapid adoptions that they need? >> Yeah, so we're a full suite provider. So, we're focused on digital operations, which is digitizing your back office from your workplace services to your hybrid infrastructure network, et cetera. All the way through bringing what we refer to as journey to the cloud. So how do we help you identify what applications and what data you need in the cloud. CX and EX very big focuses for us. In fact, we take a lot of pride in, while we do go to market and sell CX specifically, we consider CX part of everything we do. So if we're talking about workplace services or hybrid infrastructure or security, we want the employee experience to be solid, and we want the employee experience to be consistent across all of those things. So, we think that our customers should not expect to have different interfaces and different portals and different user experiences when they do work with us across infrastructure, application and cloud, et cetera. >> That's excellent Eric. You know we spent the last six months talking about how did we react to the pandemic, and now at least here in the US, the children are back in school. If they're back though, it tends to be a hybrid model. And when we look at work, often we know we're going to have this long gated, kind of new abnormal if you will. So, yes you might be back in the office some, but chances are you will spend some time remote and therefore it's not work from home or back to work, it's work from anywhere, is what I need to be able to do. So, how are you preparing? How are you helping your customers through that? Because it's one thing if it was just a switch that says, I'm either here or there, but it's changing and it's very fluid. >> Yeah, and you're exactly right, it is work from anywhere. But there are some of our customers that don't have the luxury of work from anywhere. So when you think about manufacturing facilities and different hospitality companies, there are people that need to go into physical places. We do a lot in the healthcare space. We need doctors in the hospitals. So we've done a lot to help our customers figure out safe ways to return to work. Recently, we've seen universities, and as you mentioned, high schools and elementary schools all going back with varying degrees of success, right? Some of them have failed and they've had to take a pause and figure out how they're going to restart. We've also seen professional sports leagues and now college sports leagues. And when we see them having issues, we see protocols adjusting and we see them looking for what can we do to make this safer, more effective and more successful for whether it's our sports team, our school or our business. So we've taken a very active approach in that. And we're leveraging technology and creating IP that starts with pre-arrival, registering in advance and opting in for things like tracking social distancing and tracking the use of masks. Then using cameras and facilities to monitor it, to make sure people that are adhering to social distancing and adhering to wearing mask. And in the event that they aren't, we can send instant notifications to their phone. If we have repeat violators, we can prohibit them from coming back to the office. So we can have very strict controls and adherence to whatever the protocols may be as the protocols change. And then the other thing that allows us to do is in the event, someone would test positive with COVID, we will know exactly who they've been within six feet of without a mask over the past X number of days. All of that is stored in the cloud for us to use for reference and use for audit purposes. So that gives us the ability to then use our app to direct all the people that the person that was positive was in contact with, let them go get tested, come back with a negative test before they returned to the office. So basically what we've done is we've created all kinds of technology using automation and AI and facial recognition to bring more safety and more security to the workplace, whatever that workplace might be. Whether again, school, university, manufacturing facility, or a hotel. >> Really interesting topic. Tracking and tracing, so critically important. We've seen in many countries around the world, that's really helped them get their arms around and control that. We talked at the top of the interview about digital means leveraging the data. And if I don't have the data, I can't respond to what's happening there. Here in the US, I haven't heard as much about the tracking and tracing. Is this a company by company thing? Do they have the expense all on them to do it? And of course it raises the concerns about, well, I'm concerned about my privacy and that balance between the public interest and my right to privacy. How do you help your customers sort through some of those issues? >> Well, privacy is definitely a big issue. And you notice that when I was explaining that I said in pre-arrival, you opt in. So the way we've approached it is, it is an opt in. So those that don't want to opt in to that kind of tracking and tracing, won't be those that will be allowed to come back to the office. And that goes back to your other point, I've worked from anywhere. Many of those people can still successfully work from anywhere. But those that feel like they're more effective, more successful or have a need to be in an office, or a need to be physically again in a manufacturing facility or a hotel, we have a way to do that safely. >> All right, well, Eric one of the things I love about research events lately like yours, is a little peek into what's coming on down the road. So, any other things you'd like to share about? You know, some of the things that are exciting you, some things we should be looking at a little bit further down the road? >> Well, I think, you know, for us as you know, we spend a significant amount of money each year on research, and we really get excited about these opportunities and these showcases. So you'll see a lot of exciting information and a lot of what's coming in the future. (indistinct) out of it right now obviously because of the time you'll see themes of safety and security, but you're also going to see just a whole lot of really thought provoking, forward thinking technology. >> You always take the opportunity, even when they're crisis out there. There's the opportunity for innovation and acceleration of what's happening. >> Yes. Eric, thanks so much, a pleasure talking with you and definitely looking forward to hearing more from the event. >> Great, thank you, enjoyed it. >> And stick with us for more coverage from Upgrade 2020, I'm Stu Miniman, thanks as always for watching the Cube. (upbeat music)

(upbeat electronic music) >> Live from San Jose, in the heart of Silicon Valley, it's theCUBE. Covering Dataworks Summit 2018, brought to you by Hortonworks. (electronic whooshing) >> Welcome back to theCUBE's live coverage of Dataworks, here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We have two guests in this segment, we have Steve Wooledge, he is the VP of Product Marketing at Arcadia Data, and Satya Ramachandran, who is the VP of Engineering at Neustar. Thanks so much for coming on theCUBE. >> Our pleasure and thank you. >> So let's start out by setting the scene for our viewers. Tell us a little bit about what Arcadia Data does. >> Arcadia Data is focused on getting business value from these modern scale-out architectures, like Hadoop, and the Cloud. We started in 2012 to solve the problem of how do we get value into the hands of the business analysts that understand a little bit more about the business, in addition to empowering the data scientists to deploy their models and value to a much broader audience. So I think that's been, in some ways, the last mile of value that people need to get out of Hadoop and data lakes, is to get it into the hands of the business. So that's what we're focused on. >> And start seeing the value, as you said. >> Yeah, seeing is believing, a picture is a thousand words, all those good things. And what's really emerging, I think, is companies are realizing that traditional BI technology won't solve the scale and user concurrency issues, because architecturally, big data's different, right? We're on the scale-out, MPP architectures now, like Hadoop, the data complexity and variety has changed, but the BI tools are still the same, and you pull the data out of the system to put it into some little micro cube to do some analysis. Companies want to go after all the data, and view the analysis across a much broader set, and that's really what we enable. >> I want to hear about the relationship between your two companies, but Satya, tell us a little about Neustar, what you do. >> Neustar is an information services company, we are built around identity. We are the premiere identity provider, the most authoritative identity provider for the US. And we built a whole bunch of services around that identity platform. I am part of the marketing solutions group, and I head the analytics engineering for marketing solutions. The product that I work on helps marketers do their annual planning, as well as their campaign or tactical planning, so that they can fine tune their campaigns on an ongoing basis. >> So how do you use Arcadia Data's primary product? >> So we are a predictive analytics platform, the reporting solution, we use Arcadia for the reporting part of it. So we have multi terabytes of advertising data in our values, and so we use Arcadia to provide fast taxes to our customers, and also very granular and explorative analysis of this data. High (mumbles) and explorative analysis of this data. >> So you say you help your customers with their marketing campaigns, so are you doing predictive analytics? And are you during churn analysis and so forth? And how does Arcadia fit into all of that? >> So we get data and then they build an activation model, which tells how the marketing spent corresponds to the revenue. We not only do historical analysis, we also do predictive, in the sense that the marketers frequently done what-if analysis, saying that, what if I moved my budget from page search to TV? And how does it affect the revenue? So all of this modeling is built by Neustar, the modeling platform is built by the Neustar, but the last mile of taking these reports and providing this explorative analysis of the results, that is provided by the reporting solution, which is Arcadia. >> Well, I mean, the thing about data analytics, is that it really is going to revolutionize marketing. That famous marketing adage of, I know my advertising works, I just don't know which half, and now we're really going to be able to figure out which half. Can you talk a little bit about return on investment and what your clients see? >> Sure, we've got some major Fortune 500 companies that have said publicly that they've realized over a billion dollars of incremental value. And that could be across both marketing analytics, and how we better treat our messaging, our brand, to reach our intended audience. There's things like supply chain and being able to more realtime analyze what-if analysis for different routes, it's things like cyber security and stopping fraud and waste and things like that at a much grander scale than what was really possible in the past. >> So we're here at Dataworks and it's the Hortonworks show. Give us a sense of the degree of your engagement or partnership with Hortonworks and participation in their partner ecosystem. >> Yeah, absolutely. Hortonworks is one of our key partners, and what we did that's different architecturally, is we built our BI server directly into the data platforms. So what I mean by that is, we take the concept of a BI server, we install it and run it on the data nodes of Hortonworks Data Platform. We inherit the security directly out of systems like Apache Ranger, so that all that administration and scale is done at Hadoop economics, if you will, and it leverages the things that are already in place. So that has huge advantages both in terms of scale, but also simplicity, and then you get the performance, the concurrency that companies need to deploy out to like, 5,000 users directly on that Hadoop cluster. So, Hortonworks is a fantastic partner for us and a large number of our customers run on Hortonworks, as well as other platforms, such as Amazon Web Services, where Satya's got his system deployed. >> At the show they announced Hortonworks Data Platform 3.0. There's containerization there, there's updates to Hive to enable it to be more of a realtime analytics, and also a data warehousing engine. In Arcadia Data, do you follow their product enhancements, in terms of your own product roadmap with any specific, fixed cycle? Are you going to be leveraging the new features in HDP 3.0 going forward to add value to your customers' ability to do interactive analysis of this data in close to realtime? >> Sure, yeah, no, because we're a native-- >> 'Cause marketing campaigns are often in realtime increasingly, especially when you're using, you know, you got a completely digital business. >> Yeah, absolutely. So we benefit from the innovations happening within the Hortonworks Data Platform. So, because we're a native BI tool that runs directly within that system, you know, with changes in Hive, or different things within HDFS, in terms of performance or compression and things like that, our customers generally benefit from that directly, so yeah. >> Satya, going forward, what are some of the problems that you want to solve for your clients? What is their biggest pain points and where do you see Neustar? >> So, data is the new island, right? So, marketers, also for them now, data is the biggest, is what they're going after. They want faster analysis, they want to be able to get to insights as fast as they can, and they want to obviously get, work on as large amount of data as possible. The variety of sources is becoming higher and higher and higher, in terms of marketing. There used to be a few channels in '70s and '80s, and '90s kind of increased, now you have like, hundreds of channels, if not thousands of channels. And they want visibility across all of that. It's the ability to work across this variety of data, increasing volume at a very high speed. Those are high level challenges that we have at Neustar. >> Great. >> So the difference, marketing attribution analysis you say is one of the core applications of your solution portfolio. How is that more challenging now than it had been in the past? We have far more marketing channels, digital and so forth, then how does the state-of-the-art of marketing attribution analysis, how is it changing to address this multiplicity of channels and media for advertising and for influencing the customer on social media and so forth? And then, you know, can you give us a sense for then, what are the necessary analytical tools needed for that? We often hear about a social graph analysis or semantic analysis, or for behavioral analytics and so forth, all of this makes it very challenging. How can you determine exactly what influences a customer now in this day and age, where, you think, you know, Twitter is an influencer over the conversation. How can you nail that down to specific, you know, KPIs or specific things to track? >> So I think, from our, like you pointed out, the variety is increasing, right? And I think the marketers now have a lot more options than what they have, and that that's a blessing, and it's also a curse. Because then I don't know where I'm going to move my marketing spending to. So, attribution right now, is still sitting at the headquarters, it's kind of sitting at a very high level and it is answering questions. Like we said, with the Fortune 100 companies, it's still answering questions to the CMOs, right? Where attribution will take us, next step is to then lower down, where it's able to answer the regional headquarters on what needs to happen, and more importantly, on every store, I'm able to then answer and tailor my attribution model to a particular store. Let's take Ford for an example, right? Now, instead of the CMO suite, but, if I'm able to go to every dealer, and I'm able to personal my attribution to that particular dealer, then it becomes a lot more useful. The challenge there is it all needs to be connected. Whatever model we are working for the dealer, needs to be connected up to the headquarters. >> Yes, and that personalization, it very much leverages the kind of things that Steve was talking about at Arcadia. Being able to analyze all the data to find those micro, micro, micro segments that can be influenced to varying degrees, so yeah. I like where you're going with this, 'cause it very much relates to the power of distributing federated big data fabrics like Hortonworks' offers. >> And so it's streaming analytics is coming to forward, and it's been talked about for the past longest period of time, but we have real use cases for streaming analytics right now. Similarly, the large volumes of the data volumes is, indeed, becoming a lot more. So both of them are doing a lot more right now. >> Yes. >> Great. >> Well, Satya and Steve, thank you so much for coming on theCUBE, this was really, really fun talking to you. >> Excellent. >> Thanks, it was great to meet you. Thanks for having us. >> I love marketing talk. >> (laughs) It's fun. I'm Rebecca Knight, for James Kobielus, stay tuned to theCUBE, we will have more coming up from our live coverage of Dataworks, just after this. (upbeat electronic music)

(lively music) >> [Announcer] Live from Boston, Massachusetts, this is the Cube, covering Sparks Summit East 2017. Brought to you by Databricks. Now, here are your hosts, Dave Vellante and George Gilbert. >> Welcome back to Sparks Summit in Boston, everybody. This is the Cube, the worldwide leader in live tech coverage. We've been here two days, wall-to-wall coverage of Sparks Summit. George Gilbert, my cohost this week, and I are going to review part two of the Wikibon Big Data Forecast. Now, it's very preliminary. We're only going to show you a small subset of what we're doing here. And so, well, let me just set it up. So, these are preliminary estimates, and we're going to look at different ways to triangulate the market. So, at Wikibon, what we try to do is focus on disruptive markets, and try to forecast those over the long term. What we try to do is identify where the traditional market research estimates really, we feel, might be missing some of the big trends. So, we're trying to figure out, what's the impact, for example, of real time. And, what's the impact of this new workload that we've been talking about around continuous streaming. So, we're beginning to put together ways to triangulate that, and we're going to show you, give you a glimpse today of what we're doing. So, if you bring up the first slide, we showed this yesterday in part one. This is our last year's big data forecast. And, what we're going to do today, is we're going to focus in on that line, that S-curve. That really represents the real time component of the market. The Spark would be in there. The Streaming analytics would be in there. Add some color to that, George, if you would. >> [George] Okay, for 60 years, since the dawn of computing, we have two ways of interacting with computers. You put your punch cards in, or whatever else and you come back and you get your answer later. That's batch. Then, starting in the early 60's, we had interactive, where you're at a terminal. And then, the big revolution in the 80's was you had a PC, but you still were either interactive either with terminal or batch, typically for reporting and things like that. What's happening is the rise of a new interaction mode. Which is continuous processing. Streaming is one way of looking at it but it might be more effective to call it continuous processing because you're not going to get rid of batch or interactive but your apps are going to have a little of each. So, what we're trying to do, since this is early, early in its life cycle, we're going to try and look at that streaming component from a couple of different angles. >> Okay, as I say, that's represented by this Ogive curve, or the S-curve. On the next slide, we're at the beginning when you think about these continuous workloads. We're at the early part of that S-curve, and of course, most of you or many of you know how the S-curve works. It's slow, slow, slow. For a lot of effort, you don't get much in return. Then you hit the steep part of that S-curve. And that's really when things start to take off. So, the challenge is, things are complex right now. That's really what this slide shows. And Spark is designed, really, to reduce some of that complexity. We've heard a lot about that, but take us through this. Look at this data flow from ingest, to explore, to process, to serve. We talked a lot about that yesterday, but this underscores the complexity in the marketplace. >> [George] Right, and while we're just looking mostly at numbers today, the point of the forecast is to estimate when the barriers, representing complexities, start to fall. And then, when we can put all these pieces together, in just explore, process, serve. When that becomes an end-to-end pipeline. When you can start taking the data in on one end, get a scientist to turn it into a model, inject it into an application, and that process becomes automated. That's when it's mature enough for the knee in the curve to start. >> And that's when we think the market's going to explode. But now so, how do you bound this. Okay, when we do forecasts, we always try to bound things. Because if they're not bounded, then you get no foundation. So, if you look at the next slide, we're trying to get a sense of real-time analytics. How big can it actually get? That's what this slide is really trying to-- >> [George] So this one was one firm's take on real-time analytics, where by 2027, they see it peaking just under-- >> [Dave] When you say one firm, you mean somebody from the technology district? >> [George] Publicly available data. And we take it as as a, since they didn't have a lot of assumptions published, we took it as, okay one data point. And then, we're going to come at it with some bottoms-up end top-down data points, and compare. >> [Dave] Okay, so the next slide we want to drill into the DBMS market and when you think about DBMS, you think about the traditional RDBMS and what we know, or the Oracle, SQL Server, IBMDB2's, etc. And then, you have this emergent NewSQL, and noSQL entrance, which are, obviously, we talked today to a number of folks. The number of suppliers is exploding. The revenue's still relatively small. Certainly small relative to the RDBMS marketplace. But, take us through what your expectations is here, and what some of the assumptions are behind this. >> [George] Okay, so the first thing to understand is the DBMS market, overall, is about $40 billion of which 30 billion goes to online transaction processing supporting real operational apps. 10 billion goes to Orlap or business intelligence type stuff. The Orlap one is shrinking materially. The online transaction processing one, new sales is shrinking materially but there's a huge maintenance stream. >> [Dave] Yeah which companies like Oracle and IBM and Microsoft are living off of that trying to fund new development. >> We modeled that declining gently and beginning to accelerate more going out into the latter years of the tenure period. >> What's driving that decline? Obviously, you've got the big sucking sound of a dup in part, is driving that. But really, increasingly it's people shifting their resources to some of these new emergent applications and workloads and new types of databases to support them right? But these are still, those new databases, you can see here, the NewSQL and noSQL still, relatively, small. A lot of it's open source. But then it starts to take off. What's your assumption there? >> So here, what's going on is, if you look at dollars today, it's, actually, interesting. If you take the noSQL databases, you take DynamoDB, you take Cassandra, Hadoop, HBase, Couchbase, Mongo, Kudu and you add all those up, it's about, with DynamoDB, it's, probably, about 1.55 billion out of a $40 billion market today. >> [Dave] Okay but it's starting to get meaningful. We were approaching two billion. >> But where it's meaningful is the unit share. If that were translated into Oracle pricing. The market would be much, much bigger. So the point it. >> Ten X? >> At least, at least. >> Okay, so in terms of work being done. If there's a measure of work being done. >> [George] We're looking at dollars here. >> Operations per second or etcetera, it would be enormous. >> Yes, but that's reflective of the fact that the data volumes are exploding but the prices are dropping precipitously. >> So do you have a metric to demonstrate that. We're, obviously, not going to show it today but. >> [George] Yes. >> Okay great, so-- >> On the business intelligence side, without naming names, the data warehouse appliance vendors are charging anywhere from 25,000 per terabyte up to, when you include running costs, as high as 100,000 a terabyte. That their customers are estimating. That's not the selling cost but that's the cost of ownership per terabyte. Whereas, if you look at, let's say Hadoop, which is comparable for the off loading some of the data warehouse work loads. That's down to the 5K per terabyte range. >> Okay great, so you expect that these platforms will have a bigger and bigger impact? What's your pricing assumption? Is prices going to go up or is it just volume's going to go through the roof? >> I'm, actually, expecting pricing. It's difficult because we're going to add more and more functionality. Volumes go up and if you add sufficient functionality, you can maintain pricing. But as volumes go up, typically, prices go down. So it's a matter of how much do these noSQL and NewSQL databases add in terms of functionality and I distinguish between them because NewSQL databases are scaled out version of Oracle or Teradata but they are based on the more open source pricing model. >> Okay and NoSQL, don't forget, stands for not only SQL, not not SQL. >> If you look at the slides, big existing markets never fall off a cliff when they're in the climb. They just slowly fade. And, eventually, that accelerates. But what's interesting here is, the data volumes could explode but the revenue associated with the NoSQL which is the dark gray and the NewSQL which is the blue. Those don't explode. You could take, what's the DBMS cost of supporting YouTube? It would be in the many, many, many billions of dollars. It would support 1/2 of an Oracle itself probably. But it's all open source there so. >> Right, so that's minimizing the opportunity is what you're saying? >> Right. >> You can see the database market is flat, certainly flattish and even declining but you do expect some growth in the out years as part of that evasion, that volume, presumably-- >> And that's the next slide which is where we've seen that growth come from. >> Okay so let's talk about that. So the next slide, again, I should have set this up better. The X-axis year is worldwide dollars and the horizontal axis is time. And we're talking here about these continuous application work loads. This new work load that you talked about earlier. So take us through the three. >> [George] There's three types of workloads that, in large part, are going to be driving most of this revenue. Now, these aren't completely, they are completely comparable to the DBMS market because some of these don't use traditional databases. Or if they do, they're Torry databases and I'll explain that. >> [Dave] Sure but if I look at the IoT Edge, the Cloud and the micro services and streaming, that's a tail wind to the database forecast in the previous slide, is that right? >> [George] It's, actually, interesting but the application and infrastructure telemetry, this is what Splunk pioneered. Which is all the torrents of data coming out of your data center and your applications and you're trying to manage what's going on. That is a database application. And we know Splunk, for 2016, was 400 million. In software revenue Hadoop was 750 million. And the various other management vendors, New Relic, AppDynamics, start ups and 5% of Azure and AWS revenue. If you add all that up, it comes out to $1.7 billion for 2016. And so, we can put a growth rate on that. And we talked to several vendors to say, okay, how much will that work load be compared to IoT Edge Cloud. And the IoT Edge Cloud is the smart devices at the Edge and the analytics are in the fog but not counting the database revenue up in the Cloud. So it's everything surrounding the Cloud. And that, actually, if you look out five years, that's, maybe, 20% larger than the app and infrastructure telemetry but growing much, much faster. Then the third one where you were talking about was this a tail wind to the database. Micro server systems streaming are very different ways of building applications from what we do now. Now, people build their logic for the application and everyone then, stores their data in this centralized external database. In micro services, you build a little piece of the app and whatever data you need, you store within that little piece of the app. And so the database requirements are, rather, primitive. And so that piece will not drive a lot of database revenue. >> So if you could go back to the previous slide, Patrick. What's driving database growth in the out years? Why wouldn't database continue to get eaten away and decline? >> [George] In broad terms, the overall database market, it staying flat. Because as prices collapse but the data volumes go up. >> [Dave] But there's an assumption in here that the NoSQL space, actually, grows in the out years. What's driving that growth? >> [George] Both the NoSQL and the NewSQL. The NoSQL, probably, is best serving capturing the IoT data because you don't need lots of fancy query capabilities for concurrency. >> [Dave] So it is a tail wind in a sense in that-- >> [George] IoT but that's different. >> [Dave] Yeah sure but you've got the overall market growing. And that's because the new stuff, NewSQL and NoSQL is growing faster than the decline of the old stuff. And it's not in the 2020 to 2022 time frame. It's not enough to offset that decline. And then they have it start growing again. You're saying that's going to be driven by IoT and other Edge use cases? >> Yes, IoT Edge and the NewSQL, actually, is where when they mature, you start to substitute them for the traditional operational apps. For people who want to write database apps not who want to write micro service based apps. >> Okay, alright good. Thank you, George, for setting it up for us. Now, we're going to be at Big Data SV in mid March? Is that right? Middle of March. And George is going to be releasing the actual final forecast there. We do it every year. We use Spark Summit to look at our preliminary numbers, some of the Spark related forecasts like continuous work loads. And then we harden those forecasts going into Big Data SV. We publish our big data report like we've done for the past, five, six, seven years. So check us out at Big Data SV. We do that in conjunction with the Strada events. So we'll be there again this year at the Fairmont Hotel. We got a bunch of stuff going on all week there. Some really good programs going on. So check out siliconangle.tv for all that action. Check out Wikibon.com. Look for new research coming out. You're going to be publishing this quarter, correct? And of course, check out siliconangle.com for all the news. And, really, we appreciate everybody watching. George, been a pleasure co-hosting with you. As always, really enjoyable. >> Alright, thanks Dave. >> Alright, to that's a rap from Sparks. We're going to try to get out of here, hit the snow storm and work our way home. Thanks everybody for watching. A great job everyone here. Seth, Ava, Patrick and Alex. And thanks to our audience. This is the Cube. We're out, see you next time. (lively music)

>> [Announcer] Live from Boston, Massachusetts, this is theCUBE, covering Spark Summit East 2017, brought to you by Databricks. Now, here are your hosts, Dave Vellante and George Gilbert. >> We're back, welcome to Boston, everybody, this is a special presentation that George Gilbert and I are going to provide to you now. SiliconANGLE Media is the umbrella brand of our company, and we've got three sub-brands. One of them is Wikibon, it's the research organization that Gorge works in, and then of course, we have theCUBE and then SiliconANGLE, which is the tech publication, and then we extensively, as you may know, use CrowdChat and other social data, but we want to drill down now on the Wikibon, Wikibon research side of things. Wikibon was the first research company ever to do a big data forecast. Many, many years ago, our friend Jeff Kelly produced that for several years, we opensourced it, and it really, I think helped the industry a lot, sort of framing the big data opportunity, and then George last year did the first Spark forecast, really Spark adoption, so what we want to do now is talk about some of the trends in the marketplace, this is going to be done in two parts, today's part one, and we're really going to talk about the overall market trends and the market conditions, and then we're going to go to part two tomorrow, where you're going to release some of the numbers, right? And we'll share some of the numbers today. So, we're going to start on the first slide here, we're going to share with you some slides. The Wikibon forecast review, and George is going to, I'm going to ask you to talk about where we are at with big data apps, everybody's saying it's peaked, big data's now going mainstream, where are we at with big data apps? >> [George] Okay, so, I want to quote, just to provide context, the former CTO on VMware, Steve Herrod. He said, "In the end, it wasn't big data, "it was big analytics." And what's interesting is that when we start thinking about it, there have been three classes of, there have been traditionally two classes of workloads, one batch, and in the context of analytics, that means running reports in the background, doing offline business intelligence, but then there was also the interactive-type work. What's emerging is something that's continuously happening, and it doesn't mean that all apps are going to be always on, it just means that there are, all apps will have a batch component, an interactive component, like with the user, and then a streaming, or continuous component. >> [Dave] So it's a new type of workload. >> Yes. >> Okay. Anything else you want to point out here? >> Yeah, what's worth mentioning, this is, it's not like it's going to burst fully-formed out of the clouds, and become sort of a new standard, there's two things that has to happen, the technology has to mature, so right now you have some pretty tough trade-offs between integration, which provides simplicity, and choice and optimization, which gives you fragmentation, and then skillset, and both of those need to develop. >> [Dave] Alright, we're going to talk about both of those a little bit later in this segment. Let's go to the next slide, which really talks to some of the high-level forecast that we released last year, so these are last year's numbers, correct? >> Yes, yes. >> [Dave] Okay, so, what's changed? You've got the ogive curve, which is sort of the streaming penetration, Spark/streaming, that's what, was last year, this is now reflective of continuous, you'll be updating that, how is this changing, what do you want us to know here? >> [George] Okay, so the key takeaways here are, first, we took three application patterns, the first being the data lake, which is sort of the original canonical repository of all your data. That never goes away, but on top of it, you layer what we were calling last year systems of engagement, which is where you've got the interactive machine learning component helping to anticipate and influence a user's decision, and then on top of that, which was the aqua color, was the self-tuning systems, which is probably more IIoT stuff, where you've got a whole ecosystem of devices and intelligence in the cloud and at the edge, and you don't necessarily need a human in the loop. But, these now, when you look at them, you can break them down as having three types of workloads, the batch, the interactive, and the continuous. >> Okay, and that is sort of a new workload here, and this is a real big theme of your research now is, we all remember, no, we don't all remember, I remember punch cards, that's the ultimate batch, and then of course, the terminals were interactive, and you think of that as closer to real time, but now, this notion of continuous, if you go to the next slide, Patrick, we can take a look at how workloads are changing, so George, take us through that dynamic. >> [George] Okay so, to understand where we're going, sometimes it helps to look at where we've come from, and the traditional workloads, if we talk about applications, they were divided into, now, we talked about sort of batch versus interactive, but now, they were also divided into online transaction processing, operational application, systems of record, and then there was the analytic side, which was reporting on it, but this was sort of backward-looking reporting, and we begin to see some convergence between the two with web and mobile apps, where a user was interacting both with the analytics that informed an interaction that they might have. That's looking backwards, and we're going to take a quick look at some of the new technologies that augmented those older application patterns. Then we're going to go look at the emergent workloads and what they look like. >> Okay so, let's have a quick conversation about this before we go on to the next segment. Hadoop obviously was batch. It really was a way, as we've talked about today and many other dates in theCUBE, a way to reduce the expense of doing data warehousing and business intelligence, I remember we were interviewing Jeff Hammerbacher, and he said, "When I was at Facebook, "my mission was to break the dependency "and the container, the storage container." So he really wanted to, needed to reduce costs, he saw that infrastructure needed to change, so if you look at the next slide, which is really sort of talking to Hadoop doing batch in traditional BI, take us through that, and then we'll sort of evolve to the future. >> Okay, so this is an example of traditional workloads, batch business intelligence, because Hadoop has not really gotten to the maturity point of view where you can really do interactive business intelligence. It's going to take a little more work. But here, you've basically put in a repository more data than you could possibly ever fit in a data warehouse, and the key is, this environment was very fragmented, there were many different engines involved, and so there was a high developer complexity, and a high operational complexity, and we're getting to the point where we can do somewhat better on the integration, and we're getting to the point where we might be able to do interactive business intelligence and start doing a little bit of advanced analytics like machine learning. >> Okay. Let's talk a little bit about why we're here, we're here 'cause it's Spark Summit, Spark was designed to simplify big data, simplify a lot of the complexity in Hadoop, so on the next slide, you've got this red line of Spark, so what is Spark's role, what does that red line represent? >> Okay, so the key takeaway from this slide is, couple things. One, it's interesting, but when you listen to Matei Zaharia, who is the creator of Spark, he said, "I built this to be a better MapReduce than MapReduce," which was the old crufty heart of Hadoop. And of course, they've stretched it far beyond their original intentions, but it's not the panacea yet, and if you put it in the context of a data lake, it can help you with what a data engineer does with exploring and munging the data, and what a data scientist might do in terms of processing the data and getting it ready for more advanced analytics, but it doesn't give you an end-to-end solution, not even within the data lake. The point of explaining this is important, because we want to explain how, even in the newer workloads, Spark isn't yet mature to handle the end-to-end integration, and by making that point, we'll show where it needs still more work, and where you have to substitute other products. >> Okay, so let's have a quick discussion about those workloads. Workloads really kind of drive everything, a lot of decisions for organizations, where to put things, and how to protect data, where the value is, so in this next slide you've got, you're juxtaposing traditional workloads with emerging workloads, so let's talk about these new continuous apps. >> Okay, so, this tees it up well, 'cause we focused on the traditional workloads. The emerging ones are where data is always coming in. You could take a big flow of data and sort of end it and bucket it, and turn it into a batch process, but now that we have the capability to keep processing it, and you want answers from it very near real time, you don't want to stop it from flowing, so the first one that took off like this was collecting telemetry about the operation and performance of your apps and your infrastructure, and Splunk sort of conquered that workload first. And then the second one, the one that everyone's talking about now is sort of Internet of Things, but more accurately, the Industrial Internet of Things, and that stream of data is, again, something you'll want to analyze and act on with as little delay as possible. The third one is interesting, asynchronous microservices. This is difficult, because this doesn't necessarily require a lot of new technology, so much as a new skillset for developers, and that's going to mean it takes off fairly slowly. Maybe new developers coming out of school will adopt it whole cloth, but this is where you don't rely on a big central database, this is where you break things into little pieces, and each piece manages itself. >> So you say the components of these arrows that you're showing in just explore processor, these are all sort of discrete elements of the data flow that you have to then integrate as a customer? >> [George] Yes, frankly, these are all steps that could be an end-to-end integrative process, but it's not yet mature enough really to do it end-to-end. For example, we don't even have a data store that can go all the way from ingest to serve, and by ingest, I mean taking the millions, potentially millions or more, events per second coming in from your Internet of Things devices, the explorer would be in that same data store, letting you visualize what's there, and process doing the analysis, and serving then is, from that same data store, letting your industrial devices, or your business intelligence workloads get real-time updates. For this to work as one whole, we need a data store, for example, that can go from end-to-end, in addition to the compute and analytic capabilities that go end-to-end. The point of this is, for continuous workloads, we do want to get to this integrated point somehow, sometime, but we're not there yet. >> Okay, let's go deeper, and take a look at the next slide, you've got this data feedback loop, and you've got this prediction on top of this, what does all that mean, let's double-click on that. >> Okay, so now we're unpacking the slide we just looked at, in that we're unpacking it into two different elements, one is what you're doing when you're running the system, and the next one will be what you're doing when you're designing it. And so for this one, what you're doing when you're running the system, I've grayed out the where's the data coming from and where's it going to, just to focus on how we're operating on the data, and again, to repeat the green part, which is storage, we don't have an end-to-end integrated store that could cost-effectively, scalably handle this whole chain of steps, but what we do have is that in the runtime, you're going to ingest the data, you're going to process it and make it ready for prediction, then there's a step that's called devops for data science, we know devops for developers, but devops for data science, as we're going to see, actually unpacks a whole 'nother level of complexity, but this devops for data science, this is where you get the prediction, of, okay, so, if this turbine is vibrating and has a heat spike, it means shut it down because something's going to fail. That's the prediction component, and the serve part then takes that prediction, and makes sure that that device gets it fast. >> So you're putting that capability in the hands of the data science component so they can effect that outcome virtually instantaneously? >> Yes, but in this case, the data scientist will have done that at design time. We're still at run time, so this is, once the data scientist has built that model, here, it's the engineer who's keeping it running. >> Yeah, but it's designed into the process, that's the devops analogy. Okay great, well let's go to that sort of next piece, which is design, so how does this all affect design, what are the implications there? >> So now, before we had ingest process, then prediction with devops for data science, and then serving, now when you're at design time, you ingest the data, and there's a whole unpacking of steps, which requires a handful, or two fistfuls of tools right now to make operate. This is to acquire the data, explore it, prepare it, model it, assess it, distribute it, all those things are today handled by a collection of tools that you have to stitch together, and then you have process at which could be typically done in Spark, where you do the analysis, and then serving it, Spark isn't ready to serve, that's typically a high-speed database, one that either has tons of data for history, or gets very, very fast updates, like a Redis that's almost like a cache. So the point of this is, we can't yet take Spark as gospel from end to end. >> Okay so, there's a lot of complexity here. >> [George] Right, that's the trade-off. >> So let's take a look at the next slide, which talks to where that complexity comes from, let's look at it first from the developer side, and then we'll look at the admin, so, so on the next slide, we're looking at the complexity from the dev perspective, explain the axes here. >> Okay, okay. So, there's two axes. If you look at the x-axis at the bottom, there's ingest, explore, process, serve. Those were the steps at a high level that we said a developer has to master, and it's going to be in separate products, because we don't have the maturity today. Then on the y-axis, we have some, but not all, this is not an exhaustive list of all the different things a developer has to deal with, with each product, so the complexity is multiplying all the steps on the y-axis, data model, addressing, programming model, persistence, all the stuff's on the y-axis, by all the products he needs on the x-axis, it's a mess, which is why it's very, very hard to build these types of systems today. >> Well, and why everybody's pushing on this whole unified integration, that was a major thing that we heard throughout the day today. What about from the admin's side, let's take a look at the next slide, which is our last slide, in terms of the operational complexity, take us through that. >> [George] Okay, so, the admin is when the system's running, and reading out the complexity, or inferring the complexity, follows the same process. On the y-axis, there's a separate set of tasks. These are admin-related. Governance, scheduling and orchestration, a high availability, all the different types of security, resource isolation, each of these is done differently for each product, and the products are on the x-axis, ingest, explore, process, serve, so that when you multiply those out, and again, this isn't exhaustive, you get, again, essentially a mess of complexity. >> Okay, so we got the message, if you're a practitioner of these so-called big data technologies, you're going to be dealing with more complexity, despite the industry's pace of trying to address that, but you're seeing new projects pop up, but nonetheless, it feels like the complexity curve is growing faster than customer's ability to absorb that complexity. Okay, well, is there hope? >> Yes. But here's where we've had this conundrum. The Apache opensource community has been the most amazing source of innovation I think we've ever seen in the industry, but the problem is, going back to the amazing book, The Cathedral and the Bazaar, about opensource innovation versus top-down, the cathedral has this central architecture that makes everything fit together harmoniously, and beautifully, with simplicity. But the bazaar is so much faster, 'cause it's sort of this free market of innovation. The Apache ecosystem is the bazaar, and the burden is on the developer and the administrator to make it work together, and it was most appropriate for the big internet companies that had the skills to do that. Now, the companies that are distributing these Apache opensource components are doing a Herculean job of putting them together, but they weren't designed to fit together. On the other hand, you've got the cloud service providers, who are building, to some extent, services that have standard APIs that might've been supported by some of the Apache products, but they have proprietary implementations, so you have lock-in, but they have more of the cathedral-type architecture that-- >> And they're delivering 'em their services, even though actually, many of those data services are discrete APIs, as you point out, are proprietary. Okay, so, very useful, George, thank you, if you have questions on this presentation, you can hit Wikibon.com and fire off a question to us, we'll make sure it gets to George and gets answered. This is part one, part two tomorrow is we're going to dig into some of the numbers, right? So if you care about where the trends are, what the numbers look like, what the market size looks like, we'll be sharing that with you tomorrow, all this stuff, of course, will be available on-demand, we'll be doing CrowdChats on this, George, excellent job, thank you very much for taking us through this. Thanks for watching today, it is a wrap of day one, Spark Summit East, we'll be back live tomorrow from Boston, this is theCUBE, so check out siliconangle.com for a review of all the action today, all the news, check out Wikibon.com for all the research, siliconangle.tv is where we house all these videos, check that out, we start again tomorrow at 11 o'clock east coast time, right after the keynotes, this is theCUBE, we're at Spark Summit, #SparkSummit, we're out, see you tomorrow. (electronic music jingle)

>> live from Boston, Massachusetts. It's the Cube covering IBM Chief Data Officer Strategy Summit brought to you by IBM. Now, here are your hosts. Day Volante and Stew Minimum. >> Welcome back to Boston, everybody. This is the Cube, the worldwide leader in live tech coverage. Stillman and I have pleased to have Jean Kolker on a Cuba lem. Uh, he's IBM vice president and chief data officer of the Global Technology Services division. And Seth Dobrin who's the Director of Digital Strategies. That Monsanto. You may have seen them in the news lately. Gentlemen. Welcome to the Cube, Jean. Welcome back. Good to see you guys again. Thanks. Thank you. So let's start with the customer. Seth, Let's, uh, tell us about what you're doing here, and then we'll get into your role. >> Yes. So, you know, the CDO summit has been going on for a couple of years now, and I've been lucky enoughto be participating for a couple of a year and 1/2 or so, Um, and you know, really, the nice thing about the summit is is the interaction with piers, um, and the interaction and networking with people who are facing similar challenges from a similar perspective. >> Yes, kind of a relatively new Roland topic, one that's evolved, Gene. We talked about this before, but now you've come from industry into, ah, non regulated environment. Now what's happened like >> so I think the deal is that way. We're developing some approaches, and we get in some successes in regulated environment. Right? And now I feel with And we were being client off IBM for years, right? Using their technology's approaches. Right? So and now I feel it's time for me personally to move on something different and tried to serve our power. I mean, IBM clients respected off in this striking from healthcare, but their approaches, you know, and what IBM can do for clients go across the different industries, right? And doing it. That skill that's very beneficial, I think, for >> clients. So Monsanto obviously guys do a lot of stuff in the physical world. Yeah, you're the head of digital strategy. So what does that entail? What is Monte Santo doing for digital? >> Yes, so, you know, for as head of digital strategies for Monsanto, really? My role is to number one. Help Monsanto internally reposition itself so that we behave and act like a digital companies, so leveraging data and analytics and also the cultural shifts associated with being more digital, which is that whole kind like you start out this conversation with the whole customer first approach. So what is the real impact toe? What we're doing to our customers on driving that and then based on on those things, how can we create new business opportunities for us as a company? Um, and how can we even create new adjacent markets or new revenues in adjacent areas based on technologies and things we already have existing within the company? >> It was the scope of analytics, customer engagement of digital experiences, all of the above, so that the scope is >> really looking at our portfolio across the gamut on DH, seeing how we can better serve our customers and society leveraging what we're doing today. So it's really leveraging the re use factor of the whole digital concept. Right? So we have analytics for geospatial, right? Big part of agriculture is geospatial. Are there other adjacent areas that we could apply some of that technology? Some of that learning? Can we monetize those data? We monetize the the outputs of those models based on that, Or is there just a whole new way of doing business as a company? Because we're in this digital era >> this way? Talked about a lot of the companies that have CEOs today are highly regulated. What are you learning from them? What's what's different? Kind of a new organization. You know, it might be an opportunity for you that they don't have. And, you know, do you have a CDO yet or is that something you're planning on having? >> Yes, So we don't have a CDO We do have someone acts as an essential. he's a defacto CEO, he has all of the data organizations on his team. Um, it's very recent for Monsanto, Um, and and so I think, you know, in terms of from the regular, what can we learn from, you know, there there are. It's about half financial people have non financial people, are half heavily regulated industries, and I think, you know, on the surface you would. You would think that, you know, there was not a lot of overlap, but I think the level of rigor that needs to go into governance in a financial institution that same thought process. Khun really be used as a way Teo really enable Maur R and D. Mohr you know, growth centered companies to be able to use data more broadly and so thinking of governance not as as a roadblock or inhibitor, but really thinking about governance is an enabler. How does it enable us to be more agile as it enable us to beam or innovative? Right? If if people in the company there's data that people could get access to by unknown process of known condition, right, good, bad, ugly. As long as people know they can do things more quickly because the data is there, it's available. It's curated. And if they shouldn't have access it under their current situation, what do they need to do to be able to access that data? Right. So if I would need If I'm a data scientist and I want to access data about my customers, what can I can't? What can and can't I do with that data? Number one doesn't have to be DEA Nana Mayes, right? Or if I want to access in, it's current form. What steps do I need to go through? What types of approval do I need to do to do to access that data? So it's really about removing roadblocks through governance instead of putting him in place. >> Gina, I'm curious. You know, we've been digging into you know, IBM has a very multifaceted role here. You know how much of this is platforms? How much of it is? You know, education and services. How much of it is, you know, being part of the data that your your customers you're using? >> Uh so I think actually, that different approaches to this issues. My take is basically we need Teo. I think that with even cognitive here, right and data is new natural resource worldwide, right? So data service, cognitive za za service. I think this is where you know IBM is coming from. And the BM is, you know, tradition. It was not like that, but it's under a lot of transformation as we speak. A lot of new people coming in a lot off innovation happening as we speak along. This line's off new times because cognitive with something, really you right, and it's just getting started. Data's a service is really new. It's just getting started. So there's a lot to do. And I think my role specifically global technology services is you know, ah, largest by having your union that IBM, you're 30 plus 1,000,000,000 answered You okay? And we support a lot of different industries basically going across all different types of industries how to transition from offerings to new business offerings, service, integrated services. I think that's the key for us. >> Just curious, you know? Where's Monsanto with kind of the adoption of cognitive, You know what? Where are you in that journey? >> Um, so we are actually a fairly advanced in the journey In terms of using analytics. I wouldn't say that we're using cognitive per se. Um, we do use a lot of machine learning. We have some applications that on the back end run on a I So some form of artificial or formal artificial intelligence, that machine learning. Um, we haven't really gotten into what, you know, what? IBM defined his cognitive in terms of systems that you can interact with in a natural, normal course of doing voice on DH that you spend a whole lot of time constantly teaching. But we do use like I said, artificial intelligence. >> Jean I'm interested in the organizational aspects. So we have Inderpal on before. He's the global CDO, your divisional CDO you've got a matrix into your leadership within the Global Services division as well as into the chief date officer for all of IBM. Okay, Sounds sounds reasonable. He laid out for us a really excellent sort of set of a framework, if you will. This is interval. Yeah, I understand your data strategy. Identify your data store says, make those data sources trusted. And then those air sequential activities. And in parallel, uh, you have to partner with line of business. And then you got to get into the human resource planning and development piece that has to start right away. So that's the framework. Sensible framework. A lot of thought, I'm sure, went into it and a lot of depth and meaning behind it. How does that framework translate into the division? Is it's sort of a plug and play and or is there their divisional goals that are create dissonance? Can you >> basically, you know, I'm only 100 plus days in my journey with an IBM right? But I can feel that the global technology services is transforming itself into integrated services business. Okay, so it's thiss framework you just described is very applicable to this, right? So basically what we're trying to do, we're trying to become I mean, it was the case before for many industries, for many of our clients. But we I want to transform ourselves into trusted broker. So what they need to do and this framework help is helping tremendously, because again, there's things we can do in concert, you know, one after another, right to control other and things we can do in parallel. So we trying those things to be put on the agenda for our global technology services, okay. And and this is new for them in some respects. But some respects it's kind of what they were doing before, but with new emphasis on data's A service cognitive as a service, you know, major thing for one of the major things for global technology services delivery. So cognitive delivery. That's kind of new type off business offerings which we need to work on how to make it truly, you know, once a sense, you know, automated another sense, you know, cognitive and deliver to our clients some you value and on value compared to what was done up until recently. What >> do you mean by cognitive delivery? Explained that. >> Yeah, so basically in in plain English. So what's right now happening? Usually when you have a large systems  computer IT system, which are basically supporting lot of in this is a lot of organizations corporations, right? You know, it's really done like this. So it's people run technology assistant, okay? And you know what Of decisions off course being made by people, But some of the decisions can be, you know, simple decisions. Right? Decisions, which can be automated, can standardize, normalize can be done now by technology, okay and people going to be used for more complex decisions, right? It's basically you're going toe. It turned from people around technology assisted toa technology to technology around people assisted. OK, that's very different. Very proposition, right? So, again, it's not about eliminating jobs, it's very different. It's taken off, you know, routine and automata ble part off the business right to technology and given options and, you know, basically options to choose for more complex decision making to people. That's kind of I would say approach. >> It's about scale and the scale to, of course, IBM. When when Gerstner made the decision, Tio so organized as a services company, IBM came became a global leader, if not the global leader but a services business. Hard to scale. You could scare with bodies, and the bigger it gets, the more complicated it gets, the more expensive it gets. So you saying, If I understand correctly, the IBM is using cognitive and software essentially to scale its services business where possible, assisted by humans. >> So that's exactly the deal. So and this is very different. Very proposition, toe say, compared what was happening recently or earlier? Always. You know other. You know, players. We're not building your shiny and much more powerful and cognitive, you know, empowered mouse trap. No, we're trying to become trusted broker, OK, and how to do that at scale. That's an open, interesting question, but we think that this transition from you know people around technology assisted Teo technology around people assisted. That's the way to go. >> So what does that mean to you? How does that resonate? >> Yeah, you know, I think it brings up a good point actually, you know, if you think of the whole litany of the scope of of analytics, you have everything from kind of describing what happened in the past All that to cognitive. Um, and I think you need to I understand the power of each of those and what they shouldn't should be used for. A lot of people talk. You talk. People talk a lot about predictive analytics, right? And when you hear predictive analytics, that's really where you start doing things that fully automate processes that really enable you to replace decisions that people make right, I think. But those air mohr transactional type decisions, right? More binary type decisions. As you get into things where you can apply binary or I'm sorry, you can apply cognitive. You're moving away from those mohr binary decisions. There's more transactional decisions, and you're moving mohr towards a situation where, yes, the system, the silicon brain right, is giving you some advice on the types of decisions that you should make, based on the amount of information that it could absorb that you can't even fathom absorbing. But they're still needs really some human judgment involved, right? Some some understanding of the contacts outside of what? The computer, Khun Gay. And I think that's really where something like cognitive comes in. And so you talk about, you know, in this in this move to have, you know, computer run, human assisted right. There's a whole lot of descriptive and predictive and even prescriptive analytics that are going on before you get to that cognitive decision but enables the people to make more value added decisions, right? So really enabling the people to truly add value toe. What the data and the analytics have said instead of thinking about it, is replacing people because you're never going to replace you. Never gonna replace people. You know, I think I've heard people at some of these conferences talking about, Well, no cognitive and a I is going to get rid of data scientist. I don't I don't buy that. I think it's really gonna enable data scientist to do more valuable, more incredible things >> than they could do today way. Talked about this a lot to do. I mean, machines, through the course of history, have always replaced human tasks, right, and it's all about you know, what's next for the human and I mean, you know, with physical labor, you know, driving stakes or whatever it is. You know, we've seen that. But now, for the first time ever, you're seeing cognitive, cognitive assisted, you know, functions come into play and it's it's new. It's a new innovation curve. It's not Moore's law anymore. That's driving innovation. It's how we interact with systems and cognitive systems one >> tonight. And I think, you know, I think you hit on a good point there when you said in driving innovation, you know, I've run, you know, large scale, automated process is where the goal was to reduce the number of people involved. And those were like you said, physical task that people are doing we're talking about here is replacing intellectual tasks, right or not replacing but freeing up the intellectual capacity that is going into solving intellectual tasks to enable that capacity to focus on more innovative things, right? We can teach a computer, Teo, explain ah, an area to us or give us some advice on something. I don't know that in the next 10 years, we're gonna be able to teach a computer to innovate, and we can free up the smart minds today that are focusing on How do we make a decision? Two. How do we be more innovative in leveraging this decision and applying this decision? That's a huge win, and it's not about replacing that person. It's about freeing their time up to do more valuable things. >> Yes, sure. So, for example, from my previous experience writing healthcare So physicians, right now you know, basically, it's basically impossible for human individuals, right to keep up with spaced of changes and innovations happening in health care and and by medical areas. Right? So in a few years it looks like there was some numbers that estimate that in three days you're going to, you know, have much more information for several years produced during three days. What was done by several years prior to that point. So it's basically becomes inhuman to keep up with all these innovations, right? Because of that decision is going to be not, you know, optimal decisions. So what we'd like to be doing right toe empower individuals make this decision more, you know, correctly, it was alternatives, right? That's about empowering people. It's not about just taken, which is can be done through this process is all this information and get in the routine stuff out of their plate, which is completely full. >> There was a stat. I think it was last year at IBM Insight. Exact numbers, but it's something like a physician would have to read 1,500 periodic ALS a week just to keep up with the new data innovations. I mean, that's virtually impossible. That something that you're obviously pointing, pointing Watson that, I mean, But there are mundane examples, right? So you go to the airport now, you don't need a person that the agent to give you. Ah, boarding pass. It's on your phone already. You get there. Okay, so that's that's That's a mundane example we're talking about set significantly more complicated things. And so what's The gate is the gate. Creativity is it is an education, you know, because these are step functions in value creation. >> You know, I think that's ah, what? The gate is a question I haven't really thought too much about. You know, when I approach it, you know the thinking Mohr from you know, not so much. What's the gate? But where? Where can this ad the most value um So maybe maybe I have thought about it. And the gate is value, um, and and its value both in terms of, you know, like the physician example where, you know, physicians, looking at images. And I mean, I don't even know what the error rate is when someone evaluates and memory or something. And I probably don't want Oh, right. So, getting some advice there, the value may not be monetary, but to me, it's a lot more than monetary, right. If I'm a patient on DH, there's a lot of examples like that. And other places, you know, that are in various industries. That I think that's that's the gate >> is why the value you just hit on you because you are a heat seeking value missile inside of your organisation. What? So what skill sets do you have? Where did you come from? That you have this capability? Was your experience, your education, your fortitude, >> While the answer's yes, tell all of them. Um, you know, I'm a scientist by training my backgrounds in statistical genetics. Um, and I've kind of worked through the business. I came up through the RND organization with him on Santo over the last. Almost exactly 10 years now, Andi, I've had lots of opportunities to leverage. Um, you know, Data and analytics have changed how the company operates on. I'm lucky because I'm in a company right now. That is extremely science driven, right? Monsanto is a science based company. And so being in a company like that, you don't face to your question about financial industry. I don't think you face the same barriers and Monsanto about using data and analytics in the same way you may in a financial types that you've got company >> within my experience. 50% of diagnosis being proven incorrect. Okay, so 50% 05 0/2 summation. You go to your physician twice. Once you on average, you get in wrong diagnosis. We don't know which one, by the way. Definitely need some someone. Garrett A cz Individuals as humans, we do need some help. Us cognitive, and it goes across different industries. Right, technologist? So if your server is down, you know you shouldn't worry about it because there is like system, you know, Abbas system enough, right? So think about how you can do that scale, and then, you know start imagined future, which going to be very empowering. >> So I used to get a second opinion, and now the opinion comprises thousands, millions, maybe tens of millions of opinions. Is that right? >> It's a try exactly and scale ofthe data accumulation, which you're going to help us to solve. This problem is enormous. So we need to keep up with that scale, you know, and do it properly exactly for business. Very proposition. >> Let's talk about the role of the CDO and where you see that evolving how it relates to the role of the CIA. We've had this conversation frequently, but is I'm wondering if the narratives changing right? Because it was. It's been fuzzy when we first met a couple years ago that that was still a hot topic. When I first started covering this. This this topic, it was really fuzzy. Has it come in two more clarity lately in terms of the role of the CDO versus the CIA over the CTO, its chief digital officer, we starting to see these roles? Are they more than just sort of buzzwords or grey? You know, areas. >> I think there's some clarity happening already. So, for example, there is much more acceptance for cheap date. Office of Chief Analytics Officer Teo, Chief Digital officer. Right, in addition to CEO. So basically station similar to what was with Serious 20 plus years ago and CEO Row in one sentence from my viewpoint would be How you going using leverage in it. Empower your business. Very proposition with CDO is the same was data how using data leverage and data, your date and your client's data. You, Khun, bring new value to your clients and businesses. That's kind ofthe I would say differential >> last word, you know, And you think you know I'm not a CDO. But if you think about the concept of establishing a role like that, I think I think the name is great because that what it demonstrates is support from leadership, that this is important. And I think even if you don't have the name in the organization like it, like in Monsanto, you know, we still have that executive management level support to the data and analytics, our first class citizens and their important, and we're going to run our business that way. I think that's really what's important is are you able to build the culture that enable you to leverage the maximum capability Data and analytics. That's really what matters. >> All right, We'll leave it there. Seth Gene, thank you very much for coming that you really appreciate your time. Thank you. Alright. Keep it right there, Buddy Stew and I'll be back. This is the IBM Chief Data Officer Summit. We're live from Boston right back.

>> live from Boston, Massachusetts. It's the Cube covering IBM Chief Data Officer Strategy Summit brought to you by IBM. Now here are your hosts. Day villain Day >> and stew Minimum. We're back. Welcome to Boston, Everybody. This is the IBM Chief Data Officer Summit. This is the Cube, the worldwide leader in live tech coverage. Inderpal. Bhandari is here. He's the newly appointed chief data officer at IBM. He's joined, but joined by Bob Picciano who is the senior vice president of IBM Analytics Group. Bob. Great to see again Inderpal. Welcome. Thank you. Thank you. So good event, Bob, Let's start with you. Um, you guys have been on the chief data officer kicked for several years now. You ahead of the curve. What, are you trying to achieve it? That this event? Yes. So, >> Dave, thanks again for having us here. And thanks for being here is well, tto help your audience share in what we're doing here. We've always appreciated that your commitment to help in the the masses understand all the important pulses that are going on the industry. What we're doing here is we're really moderating form between chief date officers on. We started this really on the curve. As you said 2014, where the conference was pretty small, there were some people who were actually examining the role, thinking about becoming a chief did officer. We probably had a few formal cheap date officers we're talking about, you know, maybe 100 or so people who are participating in the very 1st 1 Now you can see it's not, You know, it's it's grown much larger. We have hundreds of people, and we're doing it multiple times a year in multiple cities. But what we're really doing is bringing together a moderated form, Um, and it's a privilege to be able to do this. Uh, this is not about selling anything to anybody. This is about exchanging ideas, understanding. You know what, the challenges of the role of the opportunities which changing about the role, what's changing about the market and the landscape, what new risks might be on the horizon? What new opportunities might be on the horizon on we you know, we really liketo listen very closely to what's going on so we can, you know, maybe build better approach is to help their mother. That's through the services we provide or whether that's through the cloud capabilities were offering or whether that's new products and services that need to be developed. And so it gives us a great understanding. And we're really fortunate to have our chief data officer here, Interpol, who's doing a great job in IBM and in helping us on our mission around really becoming a cognitive enterprise and making analytics and insight on data really be central to that transformation. >> So, Dr Bhandari, new, uh, new to the chief date officer role, not nude. IBM. You worked here and came back. I was first exposed to roll maybe 45 years ago with the chief Data officer event. OK, so you come in is the chief data officer in December. Where do you start? >> So, you know, I've had the fortune of being in this role for a long time. I was one of the earliest created, the role for healthcare in two thousand six. Then I have honed that roll over three different Steve Data officer appointments at health care companies. And now I'm at IBM. So I do have, you know, I do view with the job as a craft. So it's a practitioner job and there's a craft to it. And do I answer your question? There are five things that you have to do to get moving on the job, and three of those have to be non sequentially and to must be done and powerful but everything else. So the five alarm. The first thing is you've got to develop a data strategy and data strategy is around, is focused around having an understanding ofthe how the company monetize is or plans to monetize itself. You know, what is the strategic monetization part of the company? Not so much how it monetize is data. But what is it trying to do? How is it going to make money in the future? So in the case of IBM, it's all around cognition. It's around enabling customers to become cognitive businesses. So my data strategy or our data strategy, I should say, is focused on enabling cognition becoming a cauldron of enterprise. You know, we've now realized that impacto prerequisite for cognition. So that's the data strategy piece. And that's the very first thing that needs to be done because once you understand that, then you understand what data is critical for the company, so you don't boil the ocean instead, what you do is you begin to govern exactly what's necessary and make sure it's fit for purpose. And then you can also create trusted data sources around those critical data assets that are critical for the for the monetization strategy of the company's. Those three have to go in sequence because if you don't know what you can do to adequately kind of three, and they're also significant pitfalls if you don't follow that sequence because you can end up pointing the ocean and the other two activities that must be done concurrently. One is in terms ofthe establishing deep partnerships with the other areas of the company the key business units, the key functional units because that's how you end up understanding what that data strategy ought to be. You know, if you don't have that knowledge of the company by making that effort that due diligence, that it's very difficult to get the data strategy right, so you've got to establish those partnerships and then the 5th 1 is because this is a space where you do require very significant talent. You have to start developing that talent and that all the organizational capability right from day one. >> So, Bob, you said that, uh, data is the new middle manager. You can't have an effective middle manager come unless you at least have some framework that was just described. >> Yeah, absolutely. So, you know, when Interpol talks about that fourth initiative about the engagement with the business units and making sure that we're in alignment on how the company's monetizing its value to its clients, his involvement with our team goes way beyond how he thinks about what date it is that we're collecting in the products that you're offering and what we might understand about our customers or about the marketplace. His involvement goes also into how we're curating the right user experience for who we want to win power with our products and offerings. Sometimes that's the role of the chief date officer. Sometimes that's the role of a data engineer. Sometimes it's the role of a data scientist. You mentioned data becoming the new middle management middle manager. We think the citizen analyst is ushering in that from from their seat, But we also need to be able to, from a perspective, to help them eliminate the long tail and and get transparency, the information. And sometimes it's the application developer. So we, uh, we collaborate on a very frequent basis, where, when we think about offering new capabilities to those roles, well, what's the data implication of that? What's the governance implication of that? How do we make it a seamless experience? So as people start to move down the path of igniting all of the innovation across those roles, there is a continuum to the information to using To be able to do that, how it's serving the enterprise, how it leads to that transformation to be a cognitive enterprise on DH. That's a very, very close collaboration >> we're moving from. You said you talked the process era to what I just inserted to an insight era. Yeah, um, and I have a question around that I'm not sure exactly how to formulate it, but maybe you can help. In the process, era technology was unknown. The process was very well, Don't know. Well known, but technology was mysterious. But with IBM and said help today it seems as though process is unknown. The technology's pretty known look at what uber airbnb you're doing the grabbing different technologies and putting them together. But the process is his new first of all, is that a reasonable observation? And if so, what does that mean for chief data officers? >> So the process is, you know, is new in the sense that in terms ofthe making it a cognitive process, it's going to end up being new, right? So the memorization that you >> never done it before, but it's never been done before, right >> in that sense. But it's different from process automation in the past. This is much more about knowledge, being able to scale knowledge, not just, you know, across one process, but across all the process cities that make up a company. And so in there. That goes also to the comment about data being the middle manager. I mean, if you've essentially got the ability to scale and manage knowledge, not just data but knowledge in terms of the insights that the people who are working these processes are coming up in conjunction with these data and intelligent capabilities, that that that that that of the hub right, it's the intelligence system that's had the Hubble this that's enabling all that so that That's really what leads Teo leads to the so called civilization >> way had dates to another >> important aspect of this is the process is dramatically different in the sense that it's ongoing. It's it's continuous, right, the process and your intimacy with uber and the trust that you're developing. A brand doesn't start and stop with one transaction and actually, you know branches into many different things. So your expectations, a CZ that relationships have all changed. So what they need to understand about you, what they need to protect about you, how they need to protect you in their transformation, the richness of their service needs to continue to evolve. So how they perform that task on the abundance of information they have available to perform that task. But the difficulty of being able to really consume it and make use of it is is a change. The other thing is, it's a lot more conversational, right? So the process isn't a deterministic set of steps that someone at a desk can really formulate in a business rule or a static process. It's conversationally changes. It needs to be dis ambiguity, and it needs to introduce new information during the process of disintegration. And that really, really calls upon the capabilities of a cognitive system that is rich and its ability to understand and interact with natural language to potentially introduce other sources of rich information. Because you might take a picture about what you're experiencing and all those things change that that notion from process to the conversational element. >> Dr. Bhandari, you've got an interesting role. Companies like IBM I think about the Theo with the CDO. Not only do you have your internal role, but you're also you know, a model for people going out there. You come too. Events like this. You're trying to help people in the role you've been a CDO. It's, um, health care organization to tell Yu know what's different about being kind of internal role of IBM. What kind of things? IBM Obviously, you know, strong technology culture, But tell us a little bit inside. You've learned what anything surprise you. You know, in your time that you've been doing it. >> Oh, you know, over the course ofthe time that I've been doing the roll across four different organizations, >> I guess specifically at IBM. But what's different there? >> You know, I mean IBM, for one thing, is a the The environment has tremendous scale. And if you're essentially talking about taking cognition to the enterprise, that gives us a tremendous A desperate to try out all the capabilities that were basically offering to our to our customers and to home that in the context of our own enterprise, you know, to build our own cognitive enterprise. And that's the journey that way, sharing with our with our customers and so forth. So that's that's different in in in in it. That wasn't the case in the previous previous rules that I had. And I think the other aspect that's different is the complexity of the organisation. This is a large global organization that wasn't true off the previous roles as well. They were Muchmore, not America century, you know, organizations. And so there's a There's an aspect there that also then that's complexity of the role in terms ofthe having to deal with different countries, different languages, different regulations, it just becomes much more complex. >> You first became a CDO in two thousand six, You said two thousand six, which was the same year as the Federal Rules of Civil Procedure came out and the emails became smoking guns. And then it was data viewed as a liability, and now it's completely viewed as an asset. But traditionally the CDO role was financial services and health care and government and highly regulated businesses. And it's clearly now seeping into new industries. What's driving that? Is that that value? >> Well, it is. I mean, it's, I think, that understanding that. You know, there's a tremendous natural resource in in the information in the data. But there is, you know, very much you know, union Yang around that notion of being responsible. I mean, one of the things that we're very proud of is the type of trust that we established over 105 year journey with our clients in the types of interactions we have with one another, the level of intimacy that we have in their business and very foundation away, that we serve them on. So we can never, ever do anything to compromise that you know. So the focus on really providing the ability to do the necessary governance and to do the necessary data providence and lineage in cyber security while not stifling innovation and being able to push into the next horizon. Interpol mentioned the fact that IBM, in and of itself, we think of ourselves as a laboratory, a laboratory for cognitive information innovation, a laboratory for design and innovation, which is so necessary in the digital era. And I think we've done a really good job in the spaces, but we're constantly pushing the envelope. A good example of that is blockchain, a technology that you know sometimes people think about and nefarious circumstances about, You know, what it meant to the ability to launch a Silk Road or something of that nature. We looked at the innovation understanding quite a lot about it being one of the core interview innovators around it, and saw great promise in being able to transform the way people thought about, you know, clearing multiparty transactions and applied it to our own IBM credit organization To think about a very transparent hyper ledger, we could bring those multiple parties together. People could have transparency and the transactions have a great deal of access into that space, and in a very, very rapid amount of time, we're able to take our very sizable IBM credit organization and implement that hyper ledger. Also, while thinking about the data regulation, the data government's implications. I think that's a really >> That's absolutely right. I mean, I think you know, Bob mentioned the example about the IBM credit organizer Asian, but there is. There are implications far beyond that. Their applications far beyond that in the data space. You know, it affords us now the opportunity to bring together identity management. You know, the profiles that people create from data of security aspects and essentially combined all of these aspects into what will then really become a trusted source ofthe data. You know, by trusted by me, I don't mean internally, but trusted by the consumers off the data. The subject's off the data because you'll be able to do that much in a way that's absolutely appropriate, not just fit for business purpose, but also very, very respectful of the consent on DH. Those aspects the privacy aspect ofthe data. So Blockchain really is a critical technology. >> Hype alleges a great example. We're IBM edge this week. >> You're gonna be a world of Watson. >> We will be a world Watson. We had the CEO of ever ledger on and they basically brought 1,000,000 diamonds and bringing transparency for the diamond industry. It's it's fraught with, with fraud and theft and counterfeiting and >> helping preserve integrity, the industry and eliminating the blood diamonds. And they right. >> It's fascinating to see how you know this bitcoin. You know, when so many people disparaged it is a currency, but not just the currency. You know, you guys IBM saw that early on and obviously participated in the open source. Be, You know, the old saying follow the money with us is like follow the data. So if I understand correctly, your job, a CDO is to sort of super charge of the business lines with the data strategy. And then, Bob, you're job is the line of business managers the supercharge your customers, businesses with the data strategy. Is that right? Is that the right value >> chain? I think you nailed it. Yeah, that's >> one of the things people are struggling with these days is, you know, if they can get their own data in house, then they've also gotta deal with third party. That industry did everything like that. IBM's role in that data chain is really interesting. You talked this morning about kind of the Weather Channel and kind of the data play there. Yeah, you know what? What's IBM is rolling. They're going forward. >> It's one of the most exciting things. I think about how we've evolved our strategy. And, you know, we're very fortunate to have Jimmy at the helm. Who really understands, You know, that transformational landscape on DH, how partnerships really change the ability to innovate for the companies we serve on? It was very obvious in understanding our client's problems that while they had a wealth of information that we were dealing with internally, there was great promise and being able to introduce these outside signals. If you will insights from other sources of data, Sometimes I call them vectors of information that could really transform the way they were thinking about solving their customer problem. So, you know, why wouldn't you ever want to understand that customers sentiment about your brand or about the product or service? And as a consequence to that, you know, capabilities that are there on Twitter or we chat or line are essential to that, depending on where your brand is operating in your branch, probably operating in a multinational space anyway, so you have to listen to all those signals and they're all in multiple language and sentiment is very, very bespoke. It's a different language, so you have to apply sophisticated machine learning. We've invented new algorithms to understand how to glean the signal at all that white noise. You use the weather example as well. You know, we think about the economic impact of climate atmosphere, whether on business and its profound. It's 1/2 trillion dollars, you know, in each calendar year that are, you know, lost information, lost assets, lost opportunity, misplaced inventory, you know, un delivered inventory. And we think we can do a better job of helping our clients take the weather excuses out of business in a variety of different industries. And so we've focused our initiatives on that information integration, governance, understanding new analytics toe to introduce those outside signals directly in the heart and want to place it on the desk of the chief data officer of those who are innovating around information and data. >> My my joke last Columbus. If they was Dell's buying DMC, IBM is buying the weather company. What does What does that say? My question is Interpol. When when Emma happens. And Bob, when you go out and purchase companies that are data driven, what role does the chief data officer play in both em in a pre and post. >> So, you know, I think the one that there being a cop, just gonna touch on a couple of points that Bob Major and I'll address your question directly as well. Uh, in terms of the role of the chief data officer, I think you're giving me that question before how that's he walled. The one very interesting thing that's happening now with what IBM is doing is previously the chief data officer. All at least with regard to the data, Not so much the strategy, but the data itself was internal focused. You know, you kind of worried about the data you had in house or the data you're bringing in now you've gotta worry as much about the exogenous status and because, you know, that's so That's one way that that role has changed considerably and is changing and evolving, and it's creating new opportunities for us. The other is again. In the past, the chief state officer all was around creating a warehouse for analytics and separated out from the operational processes. That's changing, too, because now we've got to transform these processes themselves. So that's, you know, that's that's another expanded role to come back to. Acquisitions emanate. I mean, I view that as essentially another process that, you know, company has. And so the chief data officer role is pretty key in terms of enabling that world in terms ofthe data, but also in terms ofthe giving, you know, guidance and advice. If, for instance, the acquisition isn't that problem itself, then you know, then we would be more closely involved. But if it's beyond that in terms of being able to get the right data, do that process as well as then once you've acquired the company in being able to integrate back the critical data assets those out of the key aspect, it's an ongoing role. >> So you've got the simplest level. You've got data sources and all the things associated with that. And then you've got your algorithms and your machine learning, and we're moving beyond sort of do tow cut costs into this new era. But so hot Oh cos adjudicate. And I guess you got to do both. You've got to get new data sources and you've got to improve this continuous process. By that you talked about how do you guide your customers as to where they put their resource? No. And that's >> really Davis. You have, you know, touching out again. That's really the benefit of this sort of a forum. In this sort of a conference, it's sharing the best practices of how the top experts in the world are really wrestling with that and identifying. I think you know Interpol's framework. What do you do sequentially to build the disciplines, to build a solid corn foundation, to make the connections that are lined with the business strategy? And then what do you do concurrently along that model to continue to operate? And how do you How do you manage and make sure your stakeholders understand what's being done? What they need to continue to do to evolve the innovation and come join us here and we'll go through that in detail. But, you know, he deposited a greatjob sharing his framers of success, and I think in the other room, other CEOs are doing that now. >> Yeah, I just wanted to quickly add to Bob's comment. The framework that I described right? It has a check and balance built into it because if you are all about governance, then the Sirio role becomes very defensive in nature. It's all about making sure you within the hour, you know, within the guard rails and so forth. But you're not really moving forward in a strategic way to help the company. And and that's why you know, setting it up by driving it from the strategy don't just makes it easier to strike that plus >> clerical and more about innovation here. We talked about the D and CDO today meaning data, but really, I think about it is being a great crucible for for disruption in information because you've disruption off. I called the Chief Disruption Office under Sheriff you >> incident in Data's digitalis data. So there's that piece of Ava's Well, we have to go. I don't want to go. So that way one last question for each of you. So Interpol, uh, thinking about and you just kind of just touched on it. He's not just playing defense, you know, thinking more offense this role. Where do you want to take it. What do your you know, sort of mid term, long term goals with this role? >> It's the specific role in IBM or just in general specifically. Well, I think in the case of I B M, we have the data strategy pretty well defined. Now it's all about being able to enable a cognitive enterprise. And so in, You know, in my mind and 2 to 3 years, we'll have completely established how that ought to be done, you know, as a prescription. And we'll also have our clients essentially sharing in that in that journey so that they can go off and create cognitive enterprises themselves. So that's pretty well set. You know, I have a pretty short window to three years to make that make that happen, And I think it's it's doable. And I think it will be, you know, just just a tremendous transformation. >> Well, we're excited to be to be watching and documenting that Bob, I have to ask you a world of washing coming up. New name for new conference. We're trying to get Pepper on, trying to get Jimmy on. Say, what should we expect? Maybe could. Although it was >> coming, and I think this year we're sort of blowing the roof off on literally were getting so big that we had to move the venue. It is very much still in its core that multiple practitioner, that multiple industry event that you experienced with insight, right? So whether or not you're thinking about this and the auspices of managing your traditional environments and what you need to do to bring them into the future and how you tie these things together, that's there for you. All those great industry tracks around the product agendas and what's coming out are are there. But the level of inspiration and involvement around this cognitive innovation space is going to be front and center. We're joined by Ginny Rometty herself, who's going to be very special. Key note. We have, I think, an unprecedented lineup of industry leaders who were going to come and talk about disruption and about disruption in the cognitive era on then. And as always, the most valuable thing is the journeys that our clients are partners sharing with us about how we're leading this inflection point transformation, the industry. So I'm very much excited to see their and I hope that your audience joins us as well. >> Great. We'll Interpol. Congratulations on the new roll. Thank you. Get a couple could plug, block post out of your comments today, so I really appreciate that, Bob. Always a pleasure. Thanks so much for having us here. Really? Appreciate. >> Thanks for having us. >> Alright. Keep right, everybody, this is the Cube will be back. This is the IBM Chief Data Officer Summit. We're live from Boston. You're back. My name is Dave Volante on DH. I'm along.

>> live from Boston, Massachusetts. >> It's the Cube >> covering IBM Chief Data Officer Strategy Summit brought to you by IBM. Now, here are your hosts. Day villain Day and >> stew minimum. Welcome back to Boston, everybody. This is the IBM Chief Data Officer Summit. And this is the Cube, the worldwide leader in live tech coverage. Caitlin Lepic is here. She's an executive within the chief data officer office at IBM. And she's joined by Dave Shoot Mel, who's a research director at, uh D. C. And he covers cognitive systems and content analytics. Folks, welcome to the Cube. Good to see you. Thank you. Can't. Then we'll start with you. You were You kicked off the morning and I referenced the Forbes article or CDOs. Miracle workers. That's great. I hadn't read that article. You put up their scanned it very quickly, but you set up the event. It started yesterday afternoon at noon. You're going through, uh, this afternoon? What's it all about? This is evolved. Since, what, 2014 >> it has, um, we started our first CDO summit back in 2014. And at that time, we estimated there were maybe 200 or so CDOs worldwide, give or take and we had 30, 30 people at our first event. and we joked that we had one small corner of the conference room and we were really quite excited to start the event in 30 2014. And we've really grown. So this year we have about 170 folks joining us, 70 of which are CEOs, more acting, the studios in the organization. And so we've really been able to grow the community over the last two years and are really excited to see to see how we can continue to do that moving forward. >> And IBM has always had a big presence at the conference that we've covered the CDO event. So that's nice that you can leverage that community and continue to cultivate it. Didn't want to ask you, so it used that we were talking when we first met this morning. It used to be dated was such a wonky topic, you know, data was data value. People would try to put a value on data, and but it was just a really kind of boring but important topic. Now it's front and center with cognitive with analytics. What are you seeing in the marketplace. >> Yeah, I think. Well, what we're seeing in the market is this emphasis on predictive applications, predictive analytics, cognitive applications, artificial intelligence of deep learning. All of those those types of applications are derived and really run by data. So unless you have really good authoritative data to actually make these models work, you know, the systems aren't going to be effective. So we're seeing an emerging marketplace in both people looking at how they can leverage their first party data, which, you know, IBM is really talking about what you know, Bob Picciotto talked about this morning. But also, we're seeing thie emergency of a second party and third party data market to help build these models out even further so that I think that's what we're really seeing is the combination of the third party data along with the first party data really being the instrument for building these kind of predictive models, you know, they're going to take us hopefully, you know, far into the future. >> Okay, so, Caitlin square the circle for us. So the CDO roll generally is not perceived. Is it technology role? Correct. Yet as Davis to saying, we're talking about machine learning cognitive. Aye, aye. These air like heavy technical topics. So how does the miracle worker deal with all this stuff generally? And how does IBM deal with it inside the CDO office? Specifically? >> Sure. So it is. It's a very good point, you know, Traditionally, Seo's really have a business background, and we find that the most successful CDO sit in the business organization. So they report somewhere in a line of business. Um, and there are certainly some that have a technical background, but far more come from business background and sit in the business. I can't tell you how we are setting up our studio office at IBM. Um, so are new. And our first global chief date officer joined in December of last year. Interpol Bhandari, um and I started working for him shortly thereafter, and the way he's setting up his office is really three pillars. So first and foremost, we focused on the data engineering data sign. So getting that team in place next, it's information, governance and policy. How are we going to govern access, manage, work with data, both data that we own within our organization as well as the long list of of external data sources that that we bring in and then third is the business integration filler. So the idea is CDOs are going to be most successful when they deliver those data Science data engineering. Um, they manage and govern the data, but they pull it through the business, so ensuring that were really, you know, grounded in business unit and doing this. And so those there are three primary pillars at this point. So prior >> to formalizing the CDO role at I b m e mean remnants of these roles existed. There was a date, equality, you know, function. There was certainly governance in policy, and somebody was responsible to integrate between, you know, from the i t. To the applications, tow the business. Were those part of I t where they sort of, you know, by committee and and how did you bring all those pieces together? That couldn't have been trivial, >> and I would say it's filling. It's still going filling ongoing process. But absolutely, I would say they typically resided within particular business units, um, and so certainly have mature functions within the unit. But when we're looking for enterprise wide answers to questions about certain customers, certain business opportunities. That's where I think the role the studio really comes in and what we're What we're doing now is we are partnering very closely with business units. One example is IBM analytic. Seen it. So we're here with Bob Luciano and other business units to ensure that, as they provide us, you know, their data were able to create the single trusted source of data across the organization across the enterprise. And so I agree with you, I think, ah, lot of those capabilities and functions quite mature, they, you know, existed within units. And now it's about pulling that up to the enterprise level and then our next step. The next vision is starting to make that cognitive and starting to add some of those capabilities in particular data science, engineering, the deep learning on starting to move toward cognitive. >> Dave, I think Caitlin brought up something really interesting. We've been digging into the last couple of years is you know, there's that governance peace, but a lot of CEOs are put into that role with a mandate for innovation on. That's something that you know a lot of times it has been accused of not being all that innovative. Is that what you're seeing? You know what? Because some of the kind of is it project based or, you know, best initiatives that air driving forward with CEOs. I think what we're seeing is that enterprises they're beginning to recognize that it's not just enough to be a manufacturer. It's not just enough to be a retail organization. You need to be the one of the best one of the top two or the top three. And the only way to get to that top two or top three is to have that innovation that you're talking about and that innovation relies on having accurate data for decision making. It also relies on having accurate data for operations. So we're seeing a lot of organizations that are really, you know, looking at how data and predictive models and innovation all become part of the operational fabric of a company. Uh, you know, and if you think about the companies that are there, you know, just beating it together. You know Amazon, for example. I mean, Amazon is a completely data driven company. When you get your recommendations for, you know what to buy, or that's all coming from the data when they set up these logistics centers where they're, you know, shipping the latest supplies. They're doing that because they know where their customers are. You know, they have all this data, so they're they're integrating data into their day to day decision making. And I think that's what we're seeing, You know, throughout industry is this this idea of integrating decision data into the decision making process and elevating it? And I think that's why the CDO rule has become so much more important over the last 2 to 3 years. >> We heard this morning at 88% percent of data is dark data. Papa Geno talked about that. So thinking about the CEOs scope roll agenda, you've got data sources. You've gotto identify those. You gotta deal with data quality and then Dave, with some of the things you've been talking about, you've got predictive models that out of the box they may not be the best predictive models in the world. You've got iterated them. So how does an organization, because not every organizations like Amazon with virtually unlimited resource is capital? How does an organization balance What are you seeing in terms of getting new data sources? Refining those data source is putting my emphasis on the data vs refining and calibrating the predictive models. How organizations balancing that Maybe we start with how IBM is doing. It's what you're seeing in the field. >> So So I would say, from what we're doing from a setting up the chief data office role, we've taken a step back to say, What's the company's monitor monetization strategy? Not how your mind monetizing data. How are how are you? What's your strategy? Moving forward, Um, for Mance station. And so with IBM we've talked about it is moved to enabling cognition throughout the enterprise. And so we've really talked about taking all of your standard business processes, whether they be procurement HR finance and infusing those with cognitive and figuring out how to make those smarter. We talking examples with contracts, for example. Every organization has a lot of contracts, and right now it's, you know, quite a manual process to go through and try and discern the sorts of information you need to make better decisions and optimize the contract process. And so the idea is, you start with that strategy for us. IBM, it's cognitive. And that then dictates what sort of data sources you need. Because that's the problem you're trying to solve in the opportunity you're chasing down. And so then we talk about Okay, we've got some of that data currently residing today internally, typically in silos, typically in business units, you know, some different databases. And then what? What are longer term vision is, is we want to build the intelligence that pulls in that internal data and then really does pull in the external data that we've that we've all talked about. You know, the social data, the sentiment analysis, analysis, the weather. You know, all of that sort of external data to help us. Ultimately, in our value proposition, our mission is, you know, data driven enablement cognition. So helps us achieve our our strategy there. >> Thank you, Dad, to that. Yeah, >> I mean, I think I mean, you could take a number of examples. I mean, there's there's ah, uh, small insurance company in Florida, for example. Uh, and what they've done is they have organized their emergency situation, their emergency processing to be able to deal with tweets and to be able to deal with, you know, SMS messages and things like that. They're using sentiment analysis. They're using Tex analytics to identify where problems are occurring when hurricane happens. So they're what they're doing is they're they're organizing that kind of data and >> there and there were >> relatively small insurance company. And a lot of this is being done to the cloud, but they're basically getting that kind of sentiment analysis being ableto interpret that and add that to their decision making process. About where should I land a person? Where should I land? You know, an insurance adjuster and agent, you know, based on the tweets, that air coming in rather than than just the phone calls that air coming into the into the organization, you know? So that's a That's a simple example. And you were talking about Not everybody has the resources of an Amazon, but, you know, certainly small insurance companies, small manufacturers, small retail organizations, you, Khun get started by, you know, analyzing your You know what people are saying about you. You know, what are people saying about me on Twitter? What are people saying about me on Facebook? You know how can I use that to improve my customer service? Uh, you know, we're seeing ah whole range of solutions coming out, and and IBM actually has a broad range of solutions for things like that. But, you know, they're not the only points out there. There's there's a lot of folks do it that kind of thing, you know, in terms of the dark data analysis and barely providing that, you know, as part of the solution to help people make better decisions. >> So the answers to the questions both You're doing both new sources of data and trying to improve the the the analytics and the models. But it's a balancing act, and you could come back to the E. R. A. Y question. It sounds like IBM strategies to supercharge your existing businesses by infusing them with new data and new insights. Is >> that correctly? I would say that is correct. >> Okay, where is in many cases, the R A. Y of analytics projects that date have been a reduction on investment? You know, I'm going to move stuff from my traditional W two. A dupe is cheaper, and we feels like Dave, we're entering a new wave now maybe could talk about that a little bit. >> Yeah. I mean, I think I think there's a desk in the traditional way of measuring ROI. And I think what people are trying to do now is look at how you mentioned disruption, for example. You know what I think? Disruption is a huge opportunity. How can I increase my sales? How can I increase my revenue? How can I find new customers, you know, through these mechanisms? And I think that's what we're starting to see in the organization. And we're starting to see start ups that are dedicated to providing this level of disruption and helping address new markets. You know, by using these kinds of technologies, uh, in in new and interesting ways. I mean, everybody uses the airbnb example. Everybody uses uber example. You know that these are people who don't own cars. They don't know what hotel rooms. But, you know, they provide analytics to disrupt the hotel industry and disrupt the taxi industry. It's not just limited to those two industries. It's, you know, virtually everything you know. And I think that's what we're starting to see is this height of, uh, virtual disruption based on the dark data, uh, that people can actually begin to analyze >> within IBM. Uh, the chief data officer reports to whom. >> So the way we've set up in our organization is our CBO reports to our senior vice president of transformation and operations, who then reports to our CEO our recommendation as we talked with clients. I mean, we see this as a CEO level reporting relationship, and and oftentimes we advocate, you know, for that is where we're talking with customers and clients. It fits nicely in our organization within transformation operations, because this line is really responsible for transforming IBM. And so they're really charged with a number of initiatives throughout the organization to have better skills alignment with some of the new opportunities. To really improve process is to bring new folks on board s. So it made sense to fit within, uh, organization that the mandate is really transformation of the company of the >> and the CDO was a peer of the CIA. Is that right? Yes. >> Yes, that's right. That's right. Um, and then in our organization, the role of split and that we have a chief data officer as well as a chief analytics officer. Um, but, you know, we often see one person serving both of those roles as well. So that's kind of, you know, depend on the organizational structure of the company. >> So you can't run the business. So to grow the business, which I guess is the P and L manager's role and transformed the business, which is where the CDO comes. >> Right? Right, right. Exactly. >> I can't give you the last word. Sort of Put a bumper sticker on this event. Where do you want to see it go? In the future? >> Yes. Eso last word. You know, we try Tio, we tried a couple new things. Uh, this this year we had our deep dive breakout sessions yesterday. And the feedback I've been hearing from folks is the opportunity to talk about certain topics they really care about. Is their governance or is innovation being able to talk? How do you get started in the 1st 90 days? What? What do you do first? You know, we we have sort of a five steps that we talk through around, you know, getting your data strategy and your plan together and how you execute against that. Um And I have to tell you, those topics continue to be of interest to our to our participants every year. So we're going to continue to have those, um, and I just I love to see the community grow. I saw the first Chief data officer University, you know, announced earlier this year. I did notice a lot of PR and media around. Role of studio is miracle workers, As you mentioned, doing a lot of great work. So, you know, we're really supportive. Were big supporters of the role we'll continue to host in person events. Uh, do virtual events continue to support studios? To be successful on our big plug is will be world of Watson. Eyes are big IBM Analytics event in October, last week of October in Vegas. So we certainly invite folks to join us. There >> will be, >> and he'll be there. Right? >> Get still, try to get Jimmy on. So, Jenny, if you're watching, talking to come on the Q. >> So we do a second interview >> and we'll see. We get Teo, And I saw Hillary Mason is going to be the oh so fantastic to see her so well. Excellent. Congratulations. on being ahead of the curve with the chief date officer can theme. And I really appreciate you coming to Cube, Dave. Thank you. Thank you. All right, Keep right there. Everybody stew and I were back with our next guest. We're live from the Chief Data Officers Summit. IBM sze event in Boston Right back. My name is Dave Volante on DH. I'm a longtime industry analysts.

>> Well, quick introductions for everybody kind of out there watching in the Data Summit. I'm Ali Tierney. I am the GVP. I run EMEA Sales for Snowflake, and I'm joined today with Michael Gillespie. Quick, just to introduce himself, what he does, and the DPE come structure as it goes. Go ahead, Micheal. >> Thanks, Ali. So as you said, I'm CDTO at Domino's Pizza Enterprises. So the company that I work for, we have the franchise rights and run Australia and New Zealand, France, Belgium, Netherlands, Germany, Japan, Luxembourg, and Denmark. And that's obviously Domino's Pizza for those markets. I look after four different verticals within the business. IT for the group, Strategy and Insights where our BI team resides and has a lot to do with Snowflake. Our Store Innovations Team, our Store Innovation Operations team which look at everything from robotics in store, how to use data better in store to be working at optimum level, and our digital team which is where I started in, actually, 13 years ago. And they're guiding our digital platform at a global level and how we localize it with the local marketing teams. >> Brilliant, I'm American and I grew up with Domino's Pizza, so help me understand, kind of, from a high structure. You've been there 13 years. My growing up experience was picking up a phone and pushing buttons and calling Domino's, and clearly a ton of modernization has come in the last 20 years, and you've been with the company for 13. What have you seen as you've grown into the DPE digital kind of space and you're driving that market? How are you guys using data? What have you seen happen over the last 13 years? >> Domino is itself, or at least DPE as well, has always been a data-driven business. What we've seen, though, as we've become more of a business that utilizes digital and technology to enhance, whether the customer experience or our store operations or our enterprise team. Is the availability of data to make decisions or to actually find insights. And if I look back, I've been lucky to go on a journey of 13 years with DPE. The power of analytics and data was apparent in a digital space. And it gave us a level of insight over a purchase that we never had before. So a great example of our first use of real data in a customer experience outside callbacks people are late, where we could give real-time feedback to a customer around their progression of their order through something called Pizza Tracker, which is shared across all and used across most Domino's in the world. And they're most common for most purchasing processes. Since then, we've gone from, I could count, very easily in between this call, how many orders would make in a day online, to now over 70% of our businesses online. We have a huge amount of data coming in from different, different areas of business. And now the challenge for myself and my team is how do we make this data readily available? To the local marketing teams, local operations teams. To really get better insights on the local market. So we've just gone from having a small pool of data to a tremendous quantum of data. >> So as you look to kind of localize your markets, right? I think you just mentioned seven or eight different markets that you're in. And I would assume then you have some data sharing that goes on within DPE, right? So Belgium wants something that's different than the Netherlands that was different than Japan, right? So how are you right now democratizing that data and giving it to your customers so that your end users can see how to use that, right? In local marketing in local, kind of, business uses. >> Correct. So, we have, we have nine markets now within DPE and all those markets, every market has unique needs and wants and challenges that they're trying to solve for. So our goal is to really try to simplify the access. And that's what we talk about democratizing data. We have a series of reports so we can build customized reports so that we don't have to do as many ad hoc requests. Then when giving those dashboards having the ability to customize and benchmark where you need to. And then when it comes down to a unique customer experience that's obviously going to be a localized marketing on them because different customers bought certain, certain volumes of pizza or sides and different market that's different. So we need to make the tools that each of them and or allow our marketing teams around the world to get access to the data that they can really help them make the most informed decisions to support their franchisees and stores. >> How much of your technology has moved in general to the cloud? And then secondarily to that question, as you've moved there, and I assume significant multi-clouds because you've got so many different regions and locations, how are using Snowflake to help move data into the cloud? >> I would say from a cloud perspective we're well advanced in being clouded for a majority of our platforms or at least moving in that direction. And we're being cloud friendly economic solution and some of that data solutions for quite a while. We still have some on-premise data, like most companies, and we're in the process of migrating. And we have to be aware that we operate within markets like Europe, where GDPR is there. And we have to, we have to be well across requirements from that ability that perspective. But regardless of GDPR or not with any form of customer data or employee data or any personal data we have, we know it's a privilege to hold. So anytime we are working with data we always want to make sure that we're storing it and accessing it in the most secure way. And then beyond that, we want to make sure that, as I talked about, we want to democratize data and make it more accessible. So, you know, I'm really looking forward to seeing as we build out and continue to build out our data strategy, how we continue to work with the likes of Snowflake to just bring faster and more insightful, you know, visibility into each particular market and at a global level as well. So that our global leaders can understand how the business is performing but also get micro where they need to. >> How, as you go through your cloud journey and then and with Snowflake specifically, how did you guys look to governance and how did you look to ensure your security around data? >> Yeah. So know for us, it's all about making sure we've got the right governance and controls and processes. So working with our security team, working with the right architects on data flow and processes, working with our legal team and representatives in each market and that's vital. You know, having policies and governance around any form of activity whether it be data or around changes on the website or changes even in any operational processes is important. So. >> Yeah >> And the greatest thing is if he can, you know, through, if you're making dashboards that are unquantifiable non-personal data, you know that's a lot easier to manage, as well. Because that's giving you a representation of groups not actually down to the particular customer. >> That makes perfect sense. How have you found migrating to Snowflake? Talk through that journey a little bit and I know you're relatively early in the journeys but talk at your experience has its been so far. >> You know, the BI team, my BI team and Strategy and Insights Team have definitely been huge fans of Snowflake and the support from the team there and and the partners we're using for integration. You know, one thing that I know that, that excites me from a strategic level, it's Snowflake's ability to be cloud agnostic and for us everything we build in the future we have chosen partners that we work with in the cloud space. We shouldn't be, we should always be having that ability to be flexible or we're always going to have some fragmented data sets and the ability to utilize a solution that can stretch out into those is very important. So you know, from a strategic level that's a great level of flexibility and from a micro level, and to look at how the team operate when they're coming with stories around greater efficiency, greater flexibility, reduced processing time, reduce, reduce time, reduction in costs and certain activities. That's a great story to be told. That's what I like about this story is that they were all wins. You know, I'm getting from the team that I can run more intensive workloads now. You know, that they can they can do more immediate action. You know, they are cutting down time, as I said, something down from hours to minutes down getting some early results and that's so important. >> So, tell me what kind of business insights you're delivering back to your stakeholders when you get through this process? The quick wins. >> Yeah, well I guess it's just us being able to get reports out faster. Get information out faster, Get access to any acts, build, build bespoke things quicker. It's all about Domino's as a business that's quite an entrepreneurial fast moving. So if you can find efficiencies that, like any business, that's, that's the point. But if we can find efficiencies within our team what it means is we've got a quantum of work the team can do or a service can do, or a bucket of costs can do. If we can reduce that quantum of whether it be cost or time and human effort, that means we can output more. One thing that we're also looking at is we talked about democratizing data earlier, but how can we empower, empower teams to get insights faster? Or to go, I always think there should be no one key holder. There should be key holders of obviously the security of the data and the, and the safety and the and the rules around it. But, in regards to broad insight data or in visibility of results, we should be trying to make that as accessible as possible so that teams can find the reading sites. You've got then thousands or hundreds of people that are looking. Whether it be franchisees at store or team members that had offices in different departments. If they can get greater visibility at a top level data and drill in micro and performance, imagine the insights you continue to do or if you can get reports in their hands faster. Time in a fast moving business a day or two of lost opportunity is huge. So how do you get to make those decisions faster? And how do you stay ahead of your game? >> So as you think of data cloud and as you think of how you're going to build out a DPE specific data cloud, where do you see that going? How, where do you get where's your nirvana and end goal from your data club? >> How do we make better use of that data? So, how do we win? We know that our data repositories are only going to continue to grow. You know, we're a business that was growing at a relatively strong rate. If you look at our previous results, we have a multitude of countries. We have 2,600 stores around the world pumping out pieces every night. And that's creating different forms of data. We have 70% of our customers online. When you're capturing a continuous amount of data. One thing that we want to do is not only manage it efficiently We know that capturing data is a privilege as well, so that we're capturing the right data. And then when you're capturing the right data we still know that the quantum of that will increase. So then how we are storing it and making sure that as we add more data to our repositories we are not actually making its harder to access or it's slower to access. So it's bringing down our reports that we're continuing to optimize and what we're seeing and I touched on when you're bringing time down from hours to minutes with a tool. We're doing that. We're bringing down those solutions. So being able to manage the increasing volume of data we're getting in a more efficient way. Being able to democratize the access of it in a safe, secure, but insightful way. But, you know, having the backing of a service like Snowflake in the background, supporting access and functioning about data. Hopefully, this just means that it will give us more ability to be nimble and do more in the future. >> As you've broken down data silos with using Snowflake and started to democratize data and put it all in one spot your ML becomes richer and more able to make better decisions because you got it all out of silos at this point. >> Yeah.We've got a better floral collection about data. And we can make those data repositories more accessible or no more efficient in accessing them. It's only going to enrich our models and it's going to challenge us. I can challenge and the business can challenge the strategy and insights and BI team to look at a multitude of ways as part of supporting the business. Because they've always got a backlog of reports or solutions they want to deliver. So, we had started a journey of being a data driven company. We have started the journey of a digital company many, many years ago. >> So as we leave today Michael and we wrap up. Last question I have for you is, as you know, everybody's coming and saying do the next bread is coolest next thing. What would you recommend the users of our conference? What would you say? Like how would you, how would you say to go to market and do it the right way? >> Yeah. Let's say the main thing is for those people to reflect upon their own business and understand the challenges at hand. it's very easy to be asked, why aren't we doing AI? Why aren't we doing machine learning? Why aren't we? But those are just solutions. You should be trying to take time to say okay, but what are some of that challenges? And then can we apply those technologies to it? or could a rudimentary approach, approach of just a simple report or a very basic algorithm solve for that. But if you could take your system to the next level with ML, don't do it for ML's sake or if you could take it with a complex data extract. Make sure you've got an angle inside of what you want to deliver. And then know, once you go down the path of anything more complicated, especially with things like machine learning, that it's a never-ending story. And you're probably not going to get the result you like in the first couple of weeks or month because that's what it is. It's a learning solution. It's a ever evolving beast and you can't just throw it out there and say, "Oh, everyone will be happy." So make sure you've got a fair commitment to getting into that game. And that you've got an envision in hand, and that envision will, I can tell you, usually move once you achieve it. Because you're only going to unlock more realities or more alternative solutions that'll grow from it. >> Absolutely. >> So be strong and want the challenges. >> I love that, and it's how we like to think about the data cloud in general, right? Is we are delivering to the business. At the end of the day, data is useless if you're not giving insights and ability for your business to make decisions and move forward. So I completely agree and I really appreciate the time you took today to sit down with me and educate me on Domino's and educate the world on how you're using data to make better decisions in the business. Thanks, Michael. >> Thanks for your time.