>>with me are and Crystal Graham, a k a a C. She's the C R O of talent, and Chris Degnan is the C R. O of Snowflake. We have to go to market heavies on this section, folks. Welcome to the Cube. >>Thank you. >>Thanks for having us. >>That's our pleasure. And so let's let's talk about digital transformation, right? Everybody loves to talk about it. It zone overused term. I know, but what does it mean? Let's talk about the vision of the data cloud for snowflake and digital transformation. A. C. We've been hearing a lot about digital transformation over the past few years. It means a lot of things to a lot of people. What are you hearing from customers? How are they thinking about when I come, sometimes called DX and what's important to them? Maybe address some of the challenges even that they're facing >>Dave. That's a great question to our customers. Digital transformation literally means staying in business or not. Um, it's that simple. Um, the reality is most agree on the opportunity to modernize data management infrastructure that they need to do that to create the speed and efficiency and cost savings that digital transformation promises. But but now it's beyond that. What's become front and center for our customers is the need for trusted data, supported by an agile infrastructure that and allow a company to pivot operations as they need. Um, let me give you an example of that. One of our customers, a medical device company, was on their digital journey when Cove it hit. They started last year in 2019, and as the pandemic hit at the earlier part of this year, they really needed to take a closer look at their supply chain. On went through an entire supply chain optimization, having been completely disrupted in the you think about the logistics, the transportation, the location of where they needed to get parts, all those things when they were actually facing a need to increase production by about 20 times. In order to meet the demand on DSO, you can imagine what that required them to do and how reliant they were on clean, compliant, accurate data that they could use to make extremely critical decisions for their business. And in that situation, not just for their business but decisions. That would be the about saving lives, so the stakes have gotten a lot higher, and that's that's just one industry. It's it's really across all industries. So when you think about that, really, when you talk to any of our customers, digital transformation is really mean. It really means now having the confidence in data to support the business at critical times with accurate, trusted information. >>Chris, I've always said a key part of digital transformation is really putting data at the core of everything you know, Not not the manufacturing plant, that the core in the data around it, but putting data at the center. It seems like that's what Snowflake is bringing to the table. Can you comment? >>Yeah. I mean, I think if if I look across what's happening and especially a Z A. C said, you know, through co vid is customers are bringing more and more data sets. They wanna make smarter business decisions based on data making, data driven decisions. And we're seeing acceleration of of data moving to the cloud because they're just in abundance of data. And it's challenging to actually manage that data on premise and and as we see those those customers move those large data sets. Think what A C said is spot on is that customers don't just want to have their data in the cloud. But they actually want to understand what the data is, understand, who has access to that data, making sure that they're actually making smart business decisions based on that data. And I think that's where the partnership between both talent and stuff like are really tremendous, where you know we're helping our customers bring their data assets to to the cloud, really landing it and allowing them to do multiple, different types of workloads on top of this data cloud platform and snowflake. And then I think again what talent is bringing to the table is really helping the customer make sure that they trust the data that they're actually seeing. And I think that's a really important aspect of digital transformation today. >>Awesome and I want to get into the partnership. But I don't wanna leave the pandemic just yet. A c. I want to ask you how it's affected customer priorities and timelines with regard to modernizing their data operations and what I mean to that they think about the end and life cycle of going from raw data insights and how they're approaching those life cycles. Data quality is a key part of, you know, a good data quality. You're gonna I mean, obviously you want to reiterate, and you wanna move fast. But if if it's garbage out, then you got to start all over again. So what are you seeing in terms of the effect of the pandemic and the urgency of modernizing those data operations? >>Yeah, but like Chris just said it accelerated things for those companies that hadn't quite started their digital journey. Maybe it was something that they had budgeted for but hadn't quite resourced completely many of them. This is what it took to to really get them off the dying from that perspective, because there was no longer the the opportunity to wait. They needed to go and take care of this really critical component within their business. So, um, you know what? What Covic, I think, has taught companies have taught all of us is how vulnerable even the largest. Um, you know, companies on most robust enterprises could be those companies that had already begun Their digital transformation, maybe even years ago, had already started that process and we're in a better. We're in a great position in their journey. They fared a lot better and we're able to be agile. Were able Thio in a shift. Priorities were able to go after what they needed to do toe to run their businesses better and be able to do so with riel clarity and confidence. And I think that's really the second piece of it is, um or the last six months people's lives have really depended on the data people's lives that have really dependent on uncertainty. The pandemic has highlighted the importance of reliable and trustworthy information, not just the proliferation of data. And as Chris mentioned this data being available, it's really about making sure that you can use that data as an asset Ondas and that the greatest weapon we all have, really there is the information and good information to make a great business decisions. >>Of course, Chris, the other thing we've seen is the acceleration toe to the cloud, which is obviously you're born in the cloud. It's been a real tailwind. What are you seeing in that regard from your I was gonna say in the field, but from your zoom >>advantage. Yeah, well, I think you know, a C talked about supply chain, um, analytics in in her previous example. And I think one of the things that that we did is we hosted a data set. The covert data set over 19 data set within snowflakes, data marketplace. And we saw customers that were, you know, initially hesitant to move to the cloud really accelerate there. They're used to just snowflake in the cloud with this cove Cove. A data set on Ben. We had other customers that are, you know, in the retail space, for example, and use the cova data set to do supply chain analytics and and and accelerated. You know, it helped them make smarter business decisions on that. So So I'd say that you know, Cove, it has, you know, made customers that maybe we're may be hesitant to to start their journey in the cloud, move faster. And I've seen that, you know, really go at a blistering pace right now. >>You know, you just talked about, you know, value because it's all about value. But the old days of data quality in the early days of Chief Data, Officer all the focus was on risk avoidance. How do I get rid of data? How long do I have to keep it? And that has flipped dramatically. You know, sometime during the last decade, >>you can't get away too much from the need for quality data and and govern data. I think that's the first step. You can't really get to, um, you know, to trust the data without those components. And but to your point, the chief Data officers role, I would say, has changed pretty significantly. And in the round tables that I've participated in over the last, you know, several months. It's certainly a topic that they bring to the table that they'd like Thio chat with their peers about in terms of how they're navigating through the balance, that they still need toe to manage to the quality they still need to manage to the governance they still need. Thio ensure that that they're delivering that trusted information to the business. But now, on the flip side as well, they're being relied upon to bring new insights. And that's on bit's, um, really requiring them to work more cross functionally than they may have needed to in the past where that's been become a big part of their job is being that evangelist for data the evangelist. For that, those insights and being able to bring in new ideas for how the business can operate and identified, you know, not just not just operational efficiencies, but revenue opportunities, ways that they can shift. All you need to do is take a look at, for example, retail. You know, retail was heavily impacted by the pandemic this year on git shows how easily an industry could be could be just kind of thrown off its course simply by by a just a significant change like that. Andi need to be able to to adjust. And this is where, um when I've talked to some of the CEOs of the retail customers that we work with, they've had to really take a deep look at how they can leverage their the data at their fingertips to identify new in different ways in which they can respond to customer demands. So it's a it's a whole different dynamic. For sure, I it doesn't mean that that you walk away from the other and the original part of the role of the or the areas in which they were maybe more defined a few years ago when the role of the chief data officer became very popular. I do believe it's more of a balance at this point and really being able to deliver great value to the organization with the insights that they could bring >>well, is he stayed on that for a second. So you have this concept of data health, and I guess what kind of getting tad is that In the early days of Big Data Hadoop, it was just a lot of rogue efforts going on. People realize, Wow, there's no governance And what what seems like what snowflake and talent are trying to do is to make that the business doesn't have to worry about it. Build, build that in, don't bolt it on. But what's what's this notion of data health that you talk about? >>Companies can measure and do measure just about everything, every aspect of their business health. Um, except what's interesting is they don't have a great way to measure the health of their data, and this is an asset that they truly rely on. Their future depends on is that health of their data. And so if we take a little bit of a step back, maybe let's take a look at an example of a customer experiences to kind of make a little bit of a delineation between the differences of data, data, quality, data trust in what data health truly is. We work with a lot of health, a lot of hotel chains. And like all companies today, hotels collect a ton of information. There's mountains of information, private information about their customers through the loyalty clubs and all the information that they collect from there, the front desk, the systems that store their data. You can start to imagine the amount of information that a hotel chain has about an individual, and frequently that information has, you know, errors in it, such as duplicate entries, you know. Is it a Seagram, or is it in Chris Telegram? Same person, Slightly different, depending on how I might have looked or how I might have checked in at the time. And sometimes the data is also mismanaged, where because it's in so many different locations, it could be accessed by the wrong person of someone that wasn't necessarily intended to have that kind of visibility. And so these are examples of when you look at something like that. Now you're starting to get into, you know, privacy regulations and other kinds of things that could be really impactful to a business if data is in the wrong hands or the wrong data is in the wrong hands. So, you know, in a world of misinformation and mistrust, which is around us every single day, um, talent has really invented a way for businesses to verify the veracity, the accuracy of their data. And that's where data health really comes in Is being able to use a trust score to measure the data health on. That's what we have recently introduced is this concept of the trust score, something that can actually provide and measure, um, at the accuracy and the health of the data all the way down to an individual report. We believe that that that truly, you know, provides the explainable trust issue resolution, the kinds of things that companies are looking for in that next stage of overall data management. >>Thank you, Chris. Bring us home. So, one of the key aspects of what snowflake is doing is building out the ecosystem is very, very important. Really talk about how how you guys we're partnering and adding value in particular things that you're seeing customers do today within the ecosystem or with the help of the ecosystem and stuff like that they weren't able to do previously. >>Yeah. I mean, I think you know a C mentioned it. You mentioned it. You know, we spent I spent a lot of my zoom days talking Thio, chief data officers and as I'm talking to the chief data officers that they are so concerned their responsibility on making sure that the business users air getting accurate data so that they view that as data governance is one aspect of it. But the other aspect is the circumference of the data of where it sits and who has access to that data and making sure it's super secure. And I think you know, snowflake is a tremendous landing spot being a data warehouse or data cloud data platform as a service, you know, we take care of all the, you know, securing that data. And I think where talent really helps our customer base is helps them exactly What what is he talked about is making sure that you know myself as a business users someone like myself who's looking at data all the time, trying to make decisions on how many sales people I wanna hire house my forecast coming. You know, how's the how's the product working all that stuff? I need to make sure that I'm actually looking at at good data. And I think the combination of all sitting in a single repository like snowflake and then layering it on top or laying a tool like talent on top of it, where I can actually say, Yeah, that is good data. It helps me make smarter decisions faster. And ultimately, I think that's really where the ecosystem plays. An incredibly important, important role for snowflake in our customers, >>guys to great cast. I wish we had more time, but we gotta go on dso Thank you so much for sharing your perspectives. A great conversation

>> Announcer: Live from San Jose, it's theCUBE. Presenting Big Data, Silicon Valley, brought to you by Silicon Angle Media and its ecosystem partners. >> Welcome back to theCUBE's continuing coverage of our event Big Data SV. I'm Lisa Martin, joined by Dave Vellante and we've been here all day having some great conversations really looking at big data, cloud, AI machine-learning from many different levels. We're happy to welcome back to theCUBE one of our distinguished alumni, Ron Bodkin, who's now the Technical Director of Applied AI at Google. Hey Ron, welcome back. >> It's nice to be back Lisa, thank you. >> Yeah, thanks for coming by. >> Thanks Dave. >> So you have been a friend of theCUBE for a long time, you've been in this industry and this space for a long time. Let's take a little bit of a walk down memory lane, your perspectives on Big Data Hadoop and the evolution that you've seen. >> Sure, you know so I first got involved in big data back in 2007. I was VP in generating a startup called QuantCast in the online advertising space. You know, we were using early versions of Hadoop to crunch through petabytes of data and build data science models and I saw a huge opportunity to bring those kind of capabilities to the enterprise. You know, we were working with early Hadoop vendors. Actually, at the time, there was really only one commercial vendor of Hadoop, it was Cloudera and we were working with them and then you know, others as they came online, right? So back then we had to spend a lot of time explaining to enterprises what was this concept of big data, why it was Hadoop as an open source could get interesting, what did it mean to build a data lake? And you know, we always said look, there's going to be a ton of value around data science, right? Putting your big data together and collecting complete information and then being able to build data science models to act in your business. So you know, the exciting thing for me is you know, now we're at a stage where many companies have put those assets together. You've got access to amazing cloud scale resources like we have at Google to not only work with great information, but to start to really act on it because you know, kind of in parallel with that evolution of big data was the evolution of the algorithms as well as the access to large amounts of digital data that's propelled, you know, a lot of innovation in AI through this new trend of deep learning that we're invested heavily in. >> I mean the epiphany of Hadoop when I first heard about it was bringing, you know, five megabytes of code to a petabyte of data as sort of the bromide. But you know, the narrative in the press has really been well, they haven't really lived up to expectations, the ROI has been largely a reduction on investment and so is that fair? I mean you've worked with practitioners, you know, all your big data career and you've seen a lot of companies transform. Obviously Google as a big data company is probably the best example of one. Do you think that's a fair narrative or did the big data hype fail to live up to expectations? >> I think there's a couple of things going on here. One is, you know, that the capabilities in big data have varied widely, right? So if you look at the way, for example, at Google we operate with big data tools that we have, they're extremely productive, work at massive scale, you know, with large numbers of users being able to slice and dice and get deep analysis of data. It's a great setup for doing machine learning, right? That's why we have things like BigQuery available in the cloud. You know, I'd say that what happened in the open source Hadoop world was it ended up settling in on more of the subset of use cases around how do we make it easy to store large amounts of data inexpensively, how do we offload ETL, how do we make it possible for data scientists to get access to raw data? I don't think that's as functional as what people really had imagined coming out of big data. But it's still served a useful function complementing what companies were already doing at their warehouse, right? So I'd say those efforts to collect big data and to make them available have really been a, they've set the stage for analytic value both through better building of analytic databases but especially through machine learning. >> And there's been some clear successes. I mean, one of them obviously is advertising, Google's had a huge success there. But much more, I mean fraud detection, you're starting to see health care really glom on. Financial services have been big on this, you know, maybe largely for marketing reasons but also risk, You know for sure, so there's been some clear successes. I've likened it to, you know, before you got to paint, you got to scrape and you got to, you put in caulking and so forth. And now we're in a position where you've got a corpus of data in your organization and you can really start to apply things like machine learning and artificial intelligence. Your thoughts on that premise? >> Yeah, I definitely think there's a lot of truth to that. I think some of it was, there was a hope, a lot of people thought that big data would be magic, that you could just dump a bunch of raw data without any effort and out would come all the answers. And that was never a realistic hope. There's always a level of you have to at least have some level of structure in the data, you have to put some effort in curating the data so you have valid results, right? So it's created a set of tools to allow scaling. You know, we now take for granted the ability to have elastic data, to have it scale and have it in the cloud in a way that just wasn't the norm even 10 years ago. It's like people were thinking about very brittle, limited amounts of data in silos was the norm, so the conversation's changed so much, we almost forget how much things have evolved. >> Speaking of evolution, tell us a little bit more about your role with applied AI at Google. What was the genesis of it and how are you working with customers for them to kind of leverage this next phase of big data and applying machine learning so that they really can identify, well monetize content and data and actually identify new revenue streams? >> Absolutely, so you know at Google, we really started the journey to become an AI-first company early this decade, a little over five years ago. We invested in the Google X team, you know, Jeff Dean was one of the leaders there, sort of to invest in, hey, these deep learning algorithms are having a big impact, right? Fei-Fei Li, who's now the Chief Scientist at Google Cloud was at Stanford doing research around how can we teach a computer to see and catalog a lot of digital data for visual purposes? So combining that with advances in computing with first GPUs and then ultimately we invested in specialized hardware that made it work well for us. The massive-scale TPU's, right? That combination really started to unlock all kinds of problems that we could solve with machine learning in a way that we couldn't before. So it's now become central to all kinds of products at Google, whether it be the biggest improvements we've had in search and advertising coming from these deep learning models but also breakthroughs, products like Google Photos where you can now search and find photos based on keywords from intelligence in a machine that looks at what's in the photo, right? So we've invested and made that a central part of the business and so what we're seeing is as we build up the cloud business, there's a tremendous interest in how can we take Google's capabilities, right, our investments in open source deep learning frameworks, TensorFlow, our investments in hardware, TPU, our scalable infrastructure for doing machine learning, right? We're able to serve a billion inferences a second, right? So we've got this massive capability we've built for our own products that we're now making available for customers and the customers are saying, "How do I tap into that? "How can I work with Google, how can I work with "the products, how can I work with the capabilities?" So the applied AI team is really about how do we help customers drive these 10x opportunities with machine learning, partnering with Google? And the reason it's a 10x opportunity is you've had a big set of improvements where models that weren't useful commercially until recently are now useful and can be applied. So you can do things like translating languages automatically, like recognizing speech, like having automated dialog for chat bots or you know, all kinds of visual APIs like our AutoML API where engineers can feed up images and it will train a model specialized to their need to recognize what you're looking for, right? So those types of advances mean that all kinds of business process can be reconceived of, and dramatically improved with automation, taking a lot of human drudgery out. So customers are like "That's really "exciting and at Google you're doing that. "How do we get that, right? "We don't know how to go there." >> Well natural language processing has been amazing in the last couple of years. Not surprising that Google is so successful there. I was kind of blown away that Amazon with Alexa sort of blew past Siri, right? And so thinking about new ways in which we're going to interact with our devices, it's clearly coming, so it leads me into my question on innovation. What's driven in your view, the innovation in the last decade and what's going to drive innovation the next 10 years? >> I think innovation is very much a function of having the right kind of culture and mindset, right? So I mean for us at Google, a big part of it is what we call 10x thinking, which is really focusing on how do you think about the big problem and work on something that could have a big impact? I also think that you can't really predict what's going to work, but there's a lot of interesting ideas and many of them won't pan out, right? But the more you have a culture of failing fast and trying things and at least being open to the data and give it a shot, right, and say "Is this crazy thing going to work?" That's why we have things like Google X where we invest in moonshots but that's where, you know, throughout the business, we say hey, you can have a 20% project, you can go work on something and many of them don't work or have a small impact but then you get things like Gmail getting created out of a 20% project. It's a cultural thing that you foster and encourage people to try things and be open to the possibility that something big is on your hands, right? >> On the cultural front, it sounds like in some cases depending on the enterprise, it's a shift, in some cases it's a cultural journey. The Google on Google story sounds like it could be a blueprint, of course, how do we do this? You've done this but how much is it a blueprint on the technology capitalizing on deep learning capabilities as well as a blueprint for helping organizations on this cultural journey to be actually being able to benefit and profit from this? >> Yeah, I mean that's absolutely right Lisa that these are both really important aspects, that there's a big part of the cultural journey. In order to be an AI-first company, to really reconceive your business around what can happen with machine learning, it's important to be a digital company, right? To have a mindset of making quick decisions and thinking about how data impacts your business and activating in real time. So there's a cultural journey that companies are going through. How do we enable our knowledge workers to do this kind of work, how do we think about our products in a new way, how do we reconceive, think about automation? There's a lot of these aspects that are cultural as well, but I think a big part of it is, you know, it's easy to get overwhelmed for companies but it's like you have pick somewhere, right? What's something you can do, what's a true north, what's an area where you can start to invest and get impact and start the journey, right? Start to do pilots, start to get something going. What we found, something I've found in my career has been when companies get started with the right first project and get some success, they can build on that success and invest more, right? Whereas you know, if you're not experimenting and trying things and moving, you're never going to get there. >> Momentum is key, well Ron, thank you so much for taking some time to stop by theCUBE. I wish we had more time to chat but we appreciate your time. >> No, it's great to be here again. >> See ya. >> We want to thank you for watching theCUBE live from our event, Big Data SV in San Jose. I'm Lisa Martin with Dave Vellante, stick around we'll be back with our wrap shortly. (relaxed electronic jingle)