(upbeat music) >> Narrator: From the CUBE studios in Palo Alto, in Boston connecting with thought leaders all around the world, this is theCUBE Conversation. >> Well, there's no question these days that in the world of business, it's all about data. Data is the king. How you harvest data, how you organize your data, how you distribute your data, how you secure your data, all very important questions. And certainly a leader in the data replication business is HVR. We're joined now by their CEO, Anthony Brooks-Williams, and by Diwakar Goel, who is the Global Chief Data Officer at GE. We're going to talk about, you guessed it, data. Gentlemen, thanks for being with us. Good to have you here on theCUBE Conversation. >> Thank you. Thanks, John. >> Yeah, well, listen, >> Thanks, John. >> first off, let's just characterize the relationship between the two companies, between GE and HVR. Maybe Diwakar, let's take us back to how you got to HVR, if you will and maybe a little bit about the evolution of that relationship, where it's gone from day one. >> No, absolutely. It's now actually a long time back. It's almost five and a half years back, that we started working with Anthony. And honestly it was our early days of big data. We all had big, different kind of data warehousing platforms, but we were transitioning into the big data ecosystem and we needed a partner that could help us to get more of the real-time data. And that's when we started working with Anthony. And I would say, John, over the years, you know we have learned a lot and our partnership has grown a lot. And it's grown based on the needs. When we started, honestly just being able to replicate a lot sources and to give you context like GBG, we have the fifth largest Oracle ERP. We have the seventh largest SAP ERP. They just, just by the nature of just getting those systems in was a challenge. And we had to work through different, different solutions because some of the normal ones wouldn't work. As we got matured, and we started using data over the last two, three years, specifically, we had different challenges. The challenges was like, you know is the data completely accurate? Are we losing and dropping some data? When you're bringing three billion, five billion rows of data, every five to six hours, even if you've dropped 1% you've lost like a huge set of insights, right? So that's when you started working with Anthony more around like the nuances as to, you know what could be causing us to lose some data, or duplicate some datasets, right? And I think our partnership's been very good, because some of our use cases have been unique and we've continuously pushed Anthony and the team to deliver that. With the light of, you know these use cases are not unique, in some cases we were just ahead, just by the nature of what we were handling. >> Okay. Anthony, about then the HVR approach, Diwakar, just took us through somewhat higher level of how this relationship has evolved. It's started with big data, now, it's gone (mumbles) in terms of even fine tuning the accuracy, that's so important. Latency is obviously a huge topic too from your side of the fence. But how do you address it then? Let's take GE for example, in terms of understanding their business, learning their business, their capabilities, maybe where their holes are, you know where their weaknesses were, and showing that up. How did you approach that from the HVR side? >> Yeah. Do you mean wanting back a few years? I mean, obviously it starts, you get in there, you find an initial use case and that was moving data into a certain data warehouse platform, whether it be around analytics or reporting such as Diwakar mentioned. And that's, I mean, most commonly what we see from a lot of customers. It's, the typical use case is real-time analytics, and moving the data to an area for consolidated reporting. It's either most (indistinct) in these times, it's in the cloud. But GE you know, where that's evolved and GE are a top customer for us. We work across many of their business units of their different BUS. GE had another arm Predix, which is the industrial IOT platform that actually OEM must as well for a solution they sell to other companies in the space. But where we've worked with GE is, you know the ability one, just to support the scale, the complexity, the volume of data, many different sources systems, many different BUS, whether it be, you know, their aviation division or our divisions, or those types, to sending that data across. And the difference being as well where we've really pushed us and Diwakar and team pushed us is around the accuracy to the exact point that Diwakar mentions. This study is typically financial data. This is data that they run their business off. This is data that the executing CEOs get dashboards on a daily basis. It can't be wrong. You may not only do businesses these days, you want to make decisions on the freshest data that they can, and specifically over the last year, because that's a matter about survival. Not only is it about winning, it's about survival and doing business in the most cost-effective way. But then that type of data, that we're moving, the financial data, the financial data lags we built for GE that is capturing this out of SAP systems, where we have some other features benefits, you know that's where that really pushed us around the accuracy. And that's whereby you mean, you can't really, these, you can't ever, but especially these days, have a typical just customer tab vendor approach. It has to be a partnership. And that was one other thing Diwakar and I spoke a while ago. It was about, how do we really push and develop a partnership between the two companies, between the two organizations? And that's key. And that's where we've been pushed. And there's much new things we're working on for them based on where they are going as a business, whether it be different sources, different targets. And so that's where it's worked out well for both companies. >> So Diwakar, about the margin of error then, in terms of accuracy, 'cause I'm hearing from Anthony that this is something you really pushed them on, right? You know, and 96, 97%, doesn't cut it, right? I mean, you can't be that close. It's got to be spot on. At what point in your data journey, if you will, did it come to roost that the accuracy, you know had to improve or, you know you needed a solution that would get you where you needed to operate your various businesses? >> I think John, it basically stems down to a broader question. You know, what are you using the data for? You know, a lot of us, when we're starting this journey we want to use the data for a lot of analytical use cases. And that basically means you want to look at a broad pattern and say, okay, you know what, do I have a significant amount of inventory sitting on one plant? Or, you know, is there a bigger problem with how I'm negotiating with a vendor, and I want to change that? And for those use cases, you know getting good enough data gives you an indicator as to how do you want to work with them, right? But when you want to take your data to a far more fidelity and more critical processes, whether, you know you're trying to capture from an airplane, the latest signal, and if you had five more signal, perhaps you solve the mystery of the Malaysian Med Sync plan, or when you're trying to solve and report on your financials, right? Then the fidelity and the accuracy of data has to be spot on. And what you realize is, you know you unlock a certain set of value with analytical use cases. But if you truly want to unlock what can be done with big data, you have to go at the operational level, you have to run your business using the data real-time. It's not about like, you know, in hindsight, how can I do things better? If I want to make real-time decisions on, you know, how, what I need to make right now, what's my healthcare indicator suggesting, how do I change the dosage for a customer or a patient, right? It has to be very operational. It has to be very accurate. And that margin of error then almost becomes zero, because you are dealing with things. If you go wrong you can cost lives, right? So that's where we are. And I think honestly being able to solve that problem has now opened up a massive door of what all we can do with data. >> Yeah. Yeah, man. I think I would just build on that as well. I mean, one, it's about us as a company. We are in the data business, obviously. Sources and targets. I mean that's the table stakes stuff. What do we support? It's our ability to bridge these modern environments and the legacy environments, that we do. And you see that across all organizations. A lot of their data source sits in these legacy top environments, but that will transition to other either target systems or the new world ones that we see, more modern bleeding edge environments. So we have to support those but they're not the same time. It's building on the performance, the accuracy of the total product, versus just being able to connect the data. And that's where we get driven down the path with companies like GE, with Diwakar. And they've pushed us. But it's really bridging those environments. >> You know, it also seems like with regard to data that you look at this almost like a verb tense, what happened, what is happening, what will happen, right? So in looking at it to that person, Diwakar, if you will, in terms of the kind of information that you can glean from this vast repository of data as opposed to, you know, what did happen, what's going on right now, and then what can we make happen down the road? Where does HVR factor into that for you in terms of not only, you know, making those, having those kinds of insights, but also making sure that the right people within your organization have access to the information that they need. And maybe just, they only need. >> No, you're right, John. It's funny, you're using a different analogy but I keep referring to as taillights versus headlights, right? Gone are the days you can refer back as to what's happening. You need to just be able to look forward, right? And I think real-time data too is no longer a question or believe, it's a necessity. And I think one of the things we often miss out is real-time data is actually a very collaborative piece of how it brings the various operators together. Because in the past, if you think, if you just go a little bit old school, people will go and do their job. And then they will come back and submit what they did, right? And then you will accumulate what everybody did and make sense out of it. Now, as people are doing things live, you are hearing about it. So for example, if I am issuing payments across different, different places I need to know how much balance I need to keep in the bank, it's the simplest example, right? Now I can keep the math, I can always stack my bank with a ton of money, then I'm losing money because now I'm blocking my money. And especially now, if you think about GE which has 6,000 bank account. If I keep stacking it, I will practically go bankrupt, right? So if I have an inference of what's happening every time a payment card issued by anybody, I am knowing it real-time. It allows me to adjust for optimal liquidity. As simple as it sounds, it saves you a hundred billion dollars if you do it right, in a year, right? So I think it is just fundamentally changes. We need to think about real-time data is no longer, it's just how you need to operate. It's no longer an option. >> Yeah. You may, we see, what we've seen as posture, we were fortunate. We had a great 2020. Just under a hundred percent year-over-year growth. Why? It's about the immediacy of data, so that they can act accordingly. You mean these days, it's table stakes. You mean, it's about winning and, or just surviving compared to, you know, years ago when day old data, week old data, that was okay. You mean, then largely these legacy type (mumbles) technologies, well it was fine. It's not anymore. You mean exactly what Diwakar was saying. It's table stakes. It's just what, that's what it is. >> And I think John, in fact, I see actually it's getting further pushed out, right? Because what happens is I get real-time data from HVR but then I'm actually doing some stuff to get real-time insights after that. And there is a lag from that time to when I'm actually generating insights on which I'm acting on. Now, there is more and more of a need that how do I even shorten that cycle time, right? I actually, from it, we are getting, not only data when it's getting refresh, I actually get signals when I need to add something. So I think in fact, the need of the future is going to be also far more event-driven, where every time something happens that I need to act on, how can technologies like HVR even help you with understanding those? >> Anthony: Yes. >> Anthony, what does scale do to all this? Diwakar touched on it briefly about accuracy and all of a sudden, if you know, if you have a, if you've got a, you know a small discrepancy on a small dataset, no big deal, right? But all of a sudden, if there are issues down the road and you're talking about, you know, millions and millions and millions of inputs, now we've got a problem. So just in terms of scale and with an operation the size of GE, what kind of impacts does that have? >> Yeah. Massive. You mean, it's part of the reason why we went, why we've been successful. We have the ability to scale very well from this highly distributed architecture that we have. And so that's what's been, you know, fortunate for us over the last year, as we know. What does the stat mean? 90% of the world's data was generated over the last two years or something like that. And that just feeds into more, human scale is key. Not only complexity at scale is a key thing, and that's where we've been fortunate to set ourselves apart on that space. I mean we, GE push us and challenge us on a daily basis. The same we do with another company, the biggest online e-commerce platform, massive scale, massive scale. Then that's, we get pushed the whole time and get pushed to improve all the time. But fortunately we have a very good solution that fits into that, but it's just, and I think it just doesn't get, worse is the wrong word. It's just, it's going to continue to grow. The problem is not going away. You know, the volumes are going to increase massively. >> So Diwakar, if I could, before we wrap up here, I'm just curious from your, if you put on your forward-thinking glasses right now, in terms of the data capabilities that HVR has provided you, are they driving you to different kinds of horizons in terms of your business strategy or are your business strategies driving the data solutions that you need? I mean, which way is it right now in terms of how you are going to expand your services down the road? >> It's an interesting question. I think, and Anthony keep correcting me on this one, but today, you know because if you think about big data solutions, right? They were largely designed for a different world historically. They were designed for our IOT parametric set of data sets in different kind of world. So there was a big catch up that a lot of these companies had to do to make it relevant even for the other relational data sets, transactional data sets and everything else, right? So a big part of what I feel like Anthony and other companies have been focusing on is really making this relevant for that world. And I feel like companies like HVR are now absolutely there. And as they are there they are now starting to think about solving or I would say focusing on people who are early in their stage, and how can they get them up and quick, you know, efficient early, because that's a lot of the challenges, right? So I would say, I don't know if Anthony's focuses me in, right? So it should not be me, but it's, I think like where they're going, for example like how do they connect with all the different cloud vendors? So when a company wants to come live and if they're using data from, you know the HR Workday solution or Concord Travel solution, they can just come pitch. We are plug and play. And say, okay, enable me data from all of these and it's there. Today what took us six months to get there, right? So I think rightly so, I think Anthony and the team are focusing on that. And I think we have been partnering on with Anthony more, I would say, perhaps pushing a little more on you know, getting not only accurate data but also now on the paradigm of compliant data. Because I think what you're going to also start seeing is as companies, especially in like different kind of industries, like financial, healthcare and others, they would need data certification also of various kinds. And that would require each of these tool to meet compliance standards that were very, they were not designed for again, right? So I think that's a different paradigm, that again Anthony and the team are really doing great in helping us get there. >> Yeah. I think there's, that was good Diwakar. There's quite a bit to unpack there, you know. With companies such as GE, we've been on a journey for many years. And so that's why we deployed across the enterprise. And let's start off with, I have this source system, I'll move my data into their target system. These targets systems you know, become more frequently either data lakes or environments that were on-premise to running in the cloud, to newer platforms that are built for the cloud, like we've seen the uptake in companies like Snowflake and those types. And you mean, we see this from you know big query from Google and those type of environments. So we see those. And that's things we've got to support along the way as well. But then at the same time, more and more data starts getting generated in your non-traditional trial platforms. I mean, cloud-based applications and those things which we then support and build into this whole framework. But at the same time to what Diwakar was saying, the eyes, you know, the legal requirements, the regulator requirements on the type of data that is now being used. Before you would never typically have years ago companies moving their most valuable or their financial data into these cloud-based type environments. Well, they are today. It happens. And so with that comes a whole bunch of regulation in security. And we've certainly seen particularly this last year the uptake in when these transactions have another level of scrutiny when you're bringing in new products into these environments. So they go through, you know, basically the security and the legal requirements are a lot longer and more depth than they used to be. And that's just the typical of the areas that they're deploying these technologies in as well, and where you're taking some technologies that weren't necessarily built for the modern world that they are now adopt in the modern world. So it's quite complex and a lot to unpack there, but it's, you've got to be on top of all of that. But that's where you then work with your top customers, like at GE, that future roadmap, that feeds where one, you obviously make a decision and you go, this is where we believe the market's going, and these are the things we need to go, we know we need to go support, no matter that no customer has asked us for it yet. But the majority of it is still where customers that are pushing, bleeding edge, that are pushing you as well, and that feeds the roadmap. And, you know, there's a number of new profile platforms GE even pushed us to go support and features that Diwakar and the team have pushed us around accuracy and security and those types of things. So it's an all encompassing approach to it. >> John, we could like-- >> Actually, I think we've set up an entirely new CUBE Conversation we're going to have down the road, I think. >> Yeah. (laughing) >> Hey, gentlemen, thank you for the time. I certainly appreciate it. Really enjoyed it. And I wish you both a very happy and importantly, a healthy 2021. >> Great. >> Thank you both. Appreciate your time. >> Thank you. >> Thanks, John. >> Thank you. >> Thanks, Anthony. Bye bye. >> Bye bye. (upbeat music)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Hey, is Keith Townsend, principal at CTO Adviser, and you're watching the Cube virtual coverage of AWS reinvent 2020. I'm really excited whenever we get toe talk to actual end users. Builders. The conversation is dynamic. This is no exception. Back on the show, Al Vanish despondent head off architectures at logic I've been ish. Welcome back to the show. >>Thanks, Kate. Good to be here >>and on the other side of my screen or how you depend on how you're looking at it is Anthony Brooks Williams C E O off HBR Anthony, Welcome back to the Cube. I know your kind of tired of seeing us, but the conversation is gonna be good, I promise. >>Thanks very much. Look forward to being here and great as you said to talk about a use case for the customer in the real world. >>So I'll be let's start off by talking about lodge attacking. What are you guys doing in a W s in general? I mean, e no. Every company has public cloud, but Logitech and AWS and Public Cloud doesn't naturally come to mind. Help educate the audience. What do you guys doing? >>Sure, so traditionally, audience knows Logitech as the Mice and keyboard company, but we do have a lot of brands which are cool brands off logic tech If you know about gaming, Logitech G is a huge brand for us. We are in video collaboration space. We compete with the likes off Ciscos of the world, where we have hardware that goes on bond works with Zoom Google as well as Microsoft ecosystems. That has been a huge success in a B two b well for us. Beyond music industry gaming as an Astro gaming Jay Bird head phones for athletes. We are also in security system space. On top of that were also in the collaboration space off streaming as in stream labs so a Z can see logic has grown toe where that a lot off use cases, apart from just peripherals, is out there. We connected devices, so we're also looking to move towards a cloud ecosystem where we could be in on on our toes, toe provisioning information on DNA, make sure we are computing to the best of the world. So we are in AWS. We do a lot more in AWS now, compared to what we used to do in the past last five years has seen a change and a shift towards more cloud public cloud usage pure SAS environments in the ws as well And we provisioned data for analysis and essentially a data driven enterprise. Now more so on V as we move towards more future >>and Anthony talked to me about not necessarily just largest heck, but the larger market. How are you seeing companies such as logic? Heck take advantage off A W s and Public cloud. >>Yeah, but I think you mean ultimately we've seen it accelerated the show. Me Castle's just looking for a better way to connect with their prospects, you know, and leverage data in doing so. And we've seen this this driver around digital transformation and that's just being sped up the shirt, given what we've seen around covert and so a lot more companies have really pushed forward and adopting, you know, the infrastructure and the availability off systems and solutions that you find in a platform such as AWS on bets that we've seen grand deduction from our side of customers doing that, we provide the most efficient way of protesters to move data to so platforms such as I don't yes, and that's what we've seen. A big uptick picture. >>So let's focus the conversation around data data, the new oil. We've heard the taglines. Let's put some meat on the bone, so to speak and talk through How are you at logic Tech using real time data in the public cloud? >>Sure, Yeah. I mean, traditionally, if you look at it, uh, logic could selling hardware. Andi hope it >>works for >>the end consumer. Uh, we would not necessarily have an insight into how that product is being used. I think come fast forward. Today's world. It's a connected devices environment. You want to make sure when you sell something, it is working for that consumer. You would want them to be happy about that product, ensuring a seamless experience. Eso customer experience is big. You might want to see a repeat customer come about right. So So the intent is to have a lot off. It is connected experience where you could provisional feedback loop to the engineering team toe to ensure stability off the product, but also enhancements around that product in terms off usage patterns. And and we play a big role with hardware in what you're gaming, for example. And as you can see, that whole industry is growing toe where everything is connected. Probably people do not buy anything, which is a static discussing thing. It's all online gaming. So we want to ensure we don't add Leighton. See in the hardware that we have, ensuring a successful experience and repeat customers right? The essential intent is at the end of the day, to have success with what you sell because there's obviously other options on the market and you want to make sure our customers are happy with the hardware they are investing. Maurin that hardware platform and adding different, very fills along with it so that seamless experiences where we wanna make sure it's connected devices to get that insight. We also look at what people are saying about our products in terms off reviews on APS are on retail portals to ensure we we hear the wise off customer on channel. How's that energy in a positive way to improve the products as well as trying to figure out if there are marketing opportunities were you could go across sailing up cells, so that's essentially driving business towards that success, and at the end of it, that would essentially come up with a revenue generation model >>for us. So Anthony talked to me about how HBR fits into this, because when I look at cloud big, that can be a bit overbearing, like, where's where's the starting point? >>So I mean, for us, you mean the starting point Answer questions around. Acquiring the data data is generated in many places across organizations in many different platforms and many systems. And so we have the ability to have a very efficient technique in the way we go acquire data the way we capture data through this technique called CBC Chinese share the capture where you're feeding incremental updates off off the data across the network. That's the most efficient way to move this data. Firstly, across a wide area network cloud is an endpoint. Uh, you mean off that, And so, firstly, we specializing in supporting many different source systems and so we can acquire that data very efficiently, put it into our into a very scalable, flexible architecture that we have. That's that's a great foot for this modern world of great foot for the cloud. So not only can we capture data from many different source systems, their complexities and a lot of these type of the moments that customers have, we could take the data and move it very efficiently across that network at scale. Because we know, as you've said, data is the new oil that's the lifeblood of organizations today. So we can move that data efficiently at scale across the network and then put it into a system such a snowflake running in AWS like we do for a hobby and a larger taken. So that's really where we fit. I mean, we can, you know, we support data taken from many sources, too many different target systems. We make sure that data is highly accurate. When we move that data across that matches what was in the source of matches, what's in the in the target system. And we do that in this particular use case and what we see predominantly today, the source systems are capturing the data typically today. Still generated on Prem could be data that's sitting in an SFP environment. Unpack that data. Decode that data is to be complex to get out and understand it on moving across and put it in their target system, that predominance sitting in the cloud for all the benefits that we see that the cloud brings around elasticity and efficiency and operational costs the most type of things. And that's probably human in where we fit into this picture. >>You know, I think if I add a little bit there, right, So to Anthony's point for us, we generate a lot of data. You're looking at billions off rolls a day from the edge where people like you and I are using logic devices and we also have a lot off prp transactions That going so the three V s Typically that they call about big data is like the variety off data volume of data at velocity that you want to consume it. So the intent is if you need to be data driven, the data should be available for business consumption as it is being generated very near real time, and that the intent for some of these platforms like H we are, is How efficiently could you move that data, whether it's on Prem or a different cloud into AWS on giving it for business consumption of business analysis in near real time. So you know we strive, Toby Riel time. Whether it's data from China in our factory, on the shop floor, whether it's being generated from people like you and I playing a game for eight hours on generating so many events, we're gonna ensure all that data is being available for business analysis and gone out of those days where we would load that data once a day. And in the hope that we do a weekly analysis right today, we do analysis on make business decisions on that data as the data is being generated. And that's the key to success with such platforms, where we want to make sure we also look at build vs buy rather than us doing all that core and trying toe in just that data we obviously partner with which we are in certain application platforms to ensure stability off it. And they have proven with their experience the I P or the knowledge around how to build those platforms, which even if we go build it, we might need bigger teams to build that. I would rather rely on partners for that capability. And I bring more business value by enabling and implementing such solutions. >>So let's put a little color around that skill whenever I talk to CDOs. Chief data officers, data architects One of the biggest problems that they have in these massive systems you're talking about getting data from E. AARP uh, Internet of things devices, etcetera is simple data transformation. E t l data scientists spend a good droid at a time, maybe sometimes 80% of their time on that data transformation process that slows down the ability to get answers to critical business. Analytic questions. How is HBR assistant you guys and curling down at time for detail? >>Absolutely. So we we do not. We went to cloud about five years back, and the methodology that you talk about e t. L is sort of a point back in the day when you would do, you know, maybe a couple of times a day ingestion. So it's like in the the transition off the pipeline. As you are ingesting data, you would transform and massage the return, enhance the data and provisioned it for business consumption. Today we do lt we extract loaded into target and natively transform it as needed from business consumption. So So we look at each. We are, for example, is, uh, we're replicating all off our e r P data into snowflake in the cloud for real time ingestion and consumption. Uh, if you do all of this analysis on article side to it, typically you would have ah, processing where you would put put in a job toe, get that data out, and analysis comes back to you in a couple of hours out here, you could be slicing and dicing the data as needed on it's all self serve on provisioning. We do not build analysis foreign users. Neither do we do a lot off the data science. But we want to make sure when businesses using that data they can act on that as it's available on the example is we had a processing back in the day with demand forecasting, which we do for every product off logic for 52 weeks, looking ahead for for every week, right, and it will run for a couple of days that processing today with such platforms on in public Lot. We do that in an hour's time. Right now That's critical for business success because you want to know the methodologies you feel need Tofail or have challenges. You probably wanna have them now rather than wait a couple of days for that process in the show up, and then you do not have enough time to, at just the parameters are bringing back some other business process toe augmented. So that's what we look at. The return on investment for such investment are essentially ensuring business continuity and success outfront on faster time to deliver. >>Yeah, >>so, Anthony, this seems like this would really change the conversation within enterprises. The target customer or audience really changes from kind of this IittIe centric movement tome or strategic move. We talked to me about the conversations you've had, what customers and how this has transformed their business. >>Yeah, a few things to unpack there, um, one. You mean, obviously, customs wanna make decisions on the freshest data, so they typically relied on in the past on these batch orientated tough data movement techniques, which which will be touched on there and how we're able to reduce that that time window. Let them make decisions on the freshest data where that takes, you, choose into other parts of organizations. Because, Azzawi said, already, I mean, we know that is the lifeblood of them. There was many, I would say, Typically, I t semi, but let's call it data. Seven people sitting in the both side of organizations, if not Mawr, than used to sit in the legacy I t side. They want access to this data. They want to be able to access their daily easy. And so one of these things cloud based system SAS based systems have made that a lot easier for them. And the conversations. We have a very much driven from not only the chief data officers, but the CEOs. Now they know in order to get the advantage to win. To survive in today's times, they need to be data driven organizations, and it sounds cliche. We hear these digital transformation stories and data driven taglines. They get thrown out there, but what we've seen is where it's really it's been put to toss this year it is happening. Projects that would happen 9 12 months have been given to month Windows to happen because it's a matter of survival and so that's what's really driven. And then you also have the companies that benefit as well. You mean we're fortunate that we are able as a company globally, with composer of all to work from her very efficiently. But then support customers like Obvious who or providing these work from home technology systems that can enable another? The semester It's really moved. That's driven down from being purely I t driven to its CEO, CEO, CEO driven because its's what they've got to do. It z no longer just table stakes. >>I >>think the lines are great, right way we roll up into CEO and like I work for the CEO at at large detect. But we strive to be more service oriented than support. So I t was traditionally looked at as a support our right. But we obviously are enabling the enterprise to be data driven, so we strive to be better at what we do and how we position ourselves. As as more off service are connected to business problem, we understand the business problem and the challenge that they have on and ensuring we could find solutions and solution architectures around that problem to ensure success for that, right? And that's the key to it. Whether we build, vs, buy it. It's all about ensuring business doesn't have toe find stopgap solutions to be successful in finding a solution for their problem. >>Avi Anthony, I really appreciate you guys taking the time to peel back the layers and help the audience understand how to take thes really abstract terms and make them rial for getting answers on real time data and kind of blowing away these concepts of E t l and data transformations and how toe really put data toe work using public cloud resource sources against their real time data assets. Thank you for joining us on this installment of the Cube virtual as we cover A W s re event, make sure to check out the portal and Seymour great coverage off this exciting area off data and data analysis

>> Narrator: From theCUBE's studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hello everyone, this is Dave Vellante. Welcome to this CUBE conversation. We got a really cool company that we're going to introduce you to, and Anthony Brooks Williams is here. He's the CEO of that company, HVR. Anthony, good to see you. Thanks for coming on. >> Hey Dave, good to see you again, appreciate it. >> Yeah cheers, so tell us a little bit about HVR. Give us the background of the company, we'll get into a little bit of the history. >> Yeah sure, so at HVR we are changing the way companies routes and access their data. And as we know, data really is the lifeblood of organizations today, and if that stops moving, or stop circulating, well, there's a problem. And people want to make decisions on the freshest data. And so what we do is we move critical business data around these organizations, the most predominant place today is to the cloud, into platforms such as Snowflake, where we've seen massive traction. >> Yeah boy, have we ever. I mean, of course, last week, we saw the Snowflake IPO. The industry is abuzz with that, but so tell us a little bit more about the history of the company. What's the background of you guys? Where did you all come from? >> Sure, the company originated out of the Netherlands, at Amsterdam, founded in 2012, helping solve the issue that customer's was having moving data efficiently at scale across all across a wide area network. And obviously, the cloud is one of those endpoint. And therefore a company, such as the Dutch Postal Service personnel, where today we now move the data to Azure and AWS. But it was really around how you can efficiently move data at scale across these networks. And I have a bit of a background in this, dating back from early 2000s, when I founded a company that did auditing recovery, or SQL Server databases. And we did that through reading the logs. And so then sold that company to Golden Gate, and had that sort of foundation there, in those early days. So, I mean again, Azure haven't been moving data efficiently as we can across these organizations with it, with the key aim of allowing customers to make decisions on the freshest data. Which today's really, table stakes. >> Yeah, so, okay, so we should think about you, as I want to invoke Einstein here, move as much data as you need to, but no more, right? 'Cause it's hard to move data. So your high speeds kind of data mover, efficiency at scale. Is that how we should think about you? >> Absolutely, I mean, at our core, we are CDC trades that capture moving incremental workloads of data, moving the updates across the network, you mean, combined with the distributed architecture that's highly flexible and extensible. And these days, just that one point, customers want to make decisions on us as much as they can get. We have companies that we're doing this for, a large apparel company that's taking some of their not only their core sales data, but some of that IoT data that they get, and sort of blending that together. And given the ability to have a full view of the organization, so they can make better decisions. So it's moving as much data as they can, but also, you need to do that in a very efficient way. >> Yeah, I mean, you mentioned Snowflake, so what I'd like to do is take my old data warehouse, and whatever, let it do what it does, reporting and compliance, stuff like that, but then bring as much data as I need into my Snowflake, or whatever modern cloud database I'm using, and then apply whatever machine intelligence, and really analyze it. So really that is kind of the problem that you're solving, is getting all that data to a place where it actually can be acted on, and turned into insights, is that right? >> Absolutely, I mean, part of what we need to do is there's a whole story around multi-cloud, and that's obviously where Snowflake fit in as well. But from our point of views of supporting over 30 different platforms. I mean data is generated, data is created in a number of different source systems. And so our ability to support each of those in this very efficient way, using these techniques such as CDCs, is going to capture the data at source, and then weaving it together into some consolidated platform where they can do the type of analysis they need to do on that. And obviously, the cloud is the predominant target system of choice with something like a Snowflake there in either these clouds. I mean, we support a number of different technologies in there. But yeah, it's about getting all that data together so they can make decisions on all areas of the business. So I'd love to get into the secret sauce a little bit. I mean we've heard luminaries like Andy Jassie stand up at last year at Reinvent, he talked about Nitro, and the big pipes, and how hard it is to move data at scale. So what's the secret sauce that you guys have that allow you to be so effective at this? >> Absolutely, I mean, it starts with  how you going to acquire data? And you want to do that in the least obtrusive way to the database. So we'll actually go in, and we read the transaction logs of each of these databases. They all generate logs. And we go read the logs systems, all these different source systems, and then put it through our webs and secret sauce, and how we how we move the data, and how we compress that data as well. So, I mean, if you want to move data across a wide area network, I mean, the technique that a few companies use, such as ourselves, is change data capture. And you're moving incremental updates, incremental workloads, the change data across a network. But then combine that with the ability that we have around some of the compression techniques that we use, and, and then just into very distributed architecture, that was one of the things that made me join HVR after my previous experiences, and seeing that how that really fits in today's world of real time and cloud. I mean, those are table stakes things. >> Okay, so it's that change data capture? >> Yeah. >> Now, of course, you've got to initially seed the target. And so you do that, if I understand you use data reduction techniques, so that you're minimizing the amount of data. And then what? Do you use asynchronous methodologies, dial it down, dial it up, off hours, how does that work? >> Absolutely, exactly what you've said they mean. So we're going to we're, initially, there's an initial association, or an initial concept, where you take a copy of all of that data that sits in that source system, and replicating that over to the target system, you turn on that CDC mechanism, which is then weaving that change data. At the same time, you're compressing it, you're encrypting it, you're making sure it's highly secure, and loading that in the most efficient way into their target systems. And so we either do a lot of that, or we also work with, if there's a ETL vendor involved, that's doing some level of transformations, and they take over the transformation capabilities, or loading. We obviously do a fair amount of that ourselves as well. But it depends on what is the architecture that's in there for the customer as well. The key thing is that what we also have is, we have this compare and repair ability that's built into the product. So we will move data across, and we make sure that data that gets moved from A to B is absolutely accurate. I mean people want to know that their data can move faster, they want it to be efficient, but they also want it to be secure. They want to know that they have a peace of mind to make decisions on accurate data. And that's some stuff that we have built into the products as well, supported across all the different platforms as well. So something else that just sets us apart in that as well. >> So I want to understand the business case, if you will. I mean, is it as simple as, "Hey, we can move way more data faster. "We can do it at a lower cost." What's the business case for you guys, and the business impact? >> Absolutely, so I mean, the key thing is the business case is moving that data as efficiently as we can across this, so they can make these decisions. So our biggest online retailer in the US uses us, on the biggest busiest system. They have some standard vendors in there, but they use us, because of the scalability that we can achieve there, of making decisions on their financial data, and all the transactions that happen between the main E-commerce site, and all the third party vendors. That's us moving that data across there as efficiently as they can. And first we look at it as pretty much it's subscription based, and it's all connection based type pricing as well. >> Okay, I want to ask you about pricing. >> Yeah. >> Pricing transparency is a big topic in the industry today, but how do you how do you price? Let's start there. >> Yeah, we charge a simple per connection price. So what are the number of source systems, a connection is a source system or a target system. And we try to very simply, we try and keep it as simple as possible, and charge them on the connections. So they will buy a packet of five connections, they have source systems, two target systems. And it's pretty much as simple as that. >> You mentioned security before. So you're encrypting the data. So your data in motion's encrypted. What else do we need to know about security? >> Yeah, you mean, that we have this concept and how we handle, and we have this wallet concept, and how we integrate with the standard security systems that those customers have already, in the in this architecture. So it's something that we're constantly doing. I mean, there's there's a data encryption at rest. And initially, the whole aim is to make sure that the customer feels safe, that the data that is moving is highly secure. >> Let's talk a little bit about cloud, and maybe the architecture. Are you running in the cloud, are you running on prem, both, across clouds. How does that work? >> Yeah, all of the above. So I mean, what we see today is majority of the data is still generated on prem. And then the majority of the talks we see are in the cloud, and this is not a one time thing, this is continuous. I mean, they've moved their analytical workload into the cloud. You mean they have these large events a few times a year, and they want the ability to scale up and scale down. So we typically see you mean, right now, you need analytics, data warehouses, that type of workload is sitting in the cloud, because of the elasticity, and the scalability, and the reasons the cloud was brought on. So absolutely, we can support the cloud to cloud, we can support on prem to cloud, I think you mean, a lot of companies adopting this hybrid strategy that we've seen certainly for the foreseeable next five years. But yeah, absolutely. The source of target systems considered on prem or in the cloud. >> And where's the point of control? Is it wherever I want it to be? >> Absolutely. >> Is it in one of the clouds on prem? >> Yeah absolutely, you can put that point of control where you want it to be. We have a concept of agents, these agents search on the source and target systems. And then we have the, it's at the edge of your brain, the hub that is controlling what is happening. This data movement that can be sitting with a source system, separately, or on target system. So it's highly extensible and flexible architecture there as well. >> So if something goes wrong, it's the HVR brain that helps me recover, right? And make sure that I don't have all kinds of data corruption. Maybe you could explain that a little bit, what happens when something goes wrong? >> Yeah absolutely, I mean, we have things that are built into the product that help us highlight what has gone wrong, and how we can correct those. And then there's alerts that get sent back to us to the to the end customer. And there's been a whole bunch of training, and stuff that's taken place for then what actions they can take, but there's a lot of it is controlled through HVR core system that handles that. So we are working next step. So as we move as a service into more of an autonomous data integration model ourselves, whichever, a bunch of exciting things coming up, that just takes that off to the next levels. >> Right, well Golden Gate Heritage just sold that to Oracle, they're pretty hardcore about things like recovery. Anthony, how do you think about the market? The total available market? Can you take us through your opportunity broadly? >> Yeah absolutely, you mean, there's the core opportunity in the space that we play, as where customers want to move data, they don't want to do data integration, they want to move data from A to B. There's those that are then branching out more to moving a lot of their business workloads to the cloud on a continuous basis. And then where we're seeing a lot of traction around this particular data that resides in these critical business systems such as SAP, that is something you're asking earlier about, what are some core things on our product. We have the ability to unpack, to unlock that data that sits in some of these SAP environments. So we can go, and then decode this data that sits between these cluster pool tables, combine that with our CDC techniques, and move their data across a network. And so particularly, sort of bringing it back a little bit, what we're seeing today, people are adopting the cloud, the massive adoption of Snowflake. I mean, as we see their growth, a lot of that is driven through consumption, why? It's these big, large enterprises that are now ready to consume more. We've seen that tail wind from our perspective, as well as taking these workloads such as SAP, and moving that into something like these cloud platforms, such as a Snowflake. And so that's where we see the immediate opportunity for us. And then and then branching out from there further, but I mean, that is the core immediate area of focus right now. >> Okay, so we've talked about Snowflake a couple of times, and other platforms, they're not the only one, but they're the hot one right now. When you think about what organizations are doing, they're trying to really streamline their data pipeline to get to turn raw data into insights. So you're seeing that emerging organizations, that data pipeline, we've been talking about it for quite some time. I mean, Snowflake, obviously, is one piece of that. Where's your value in that pipeline? Is it all about getting the data into that stream? >> Yeah, you just mentioned something there that we have an issue internally that's called raw data to ready data. And that's about capturing this data, moving that across. And that's where we building value on that data as well, particularly around some of our SAP type initiatives, and solutions related to that, that we're bringing out as well. So one it's absolutely going in acquiring that data. It's then moving it as efficiently as we can at scale, which a lot of people talk about, we truly operate at scale, the biggest companies in the world use us to do that, across there and giving them that ability to make decisions on the freshest data. Therein lies the value of them being able to make decisions on data that is a few seconds, few minutes old, versus some other technology they may be using that takes hours days. You mean that is it, keeping large companies that we work with today. I mean keeping toner paper on shelves, I mean one thing that happened after COVID. I mean one of our big customers was making them out their former process, and making the shelves are full. Another healthcare provider being able to do analysis on what was happening on supplies from the hospital, and the other providers during this COVID crisis. So that's where it's a lot of that value, helping them reinvent their businesses, drive down that digital transformation strategy, is the key areas there. No data, they can't make those type of decisions. >> Yeah, so I mean, your vision really, I mean, you're betting on data. I always say don't bet against the data. But really, that's kind of the premise here. Is the data is going to continue to grow. And data, I often say data is plentiful insights aren't. And we use the Broma you said before. So really, maybe, good to summarize the vision for us, where you want to take this thing? Yeah, absolutely so we're going to continue building on what we have, making it easier to use. Certainly, as we move, as more customers move into the cloud. And then from there, I mean, we have some strategic initiatives of looking at some acquisitions as well, just to build on around offering, and some of the other core areas. But ultimately, it's getting closer to the business user. In today's world, there is many IT tech-savvy people sitting in the business side of organization, as they are in IT, if not more. And so as we go down that flow with our product, it's getting closer to those end users, because they're at the forefront of wanting this data. As we said that the data is the lifeblood of an organization. And so given an ability to drive the actual power that they need to run the data, is a core part of that vision. So we have some some strategic initiatives around some acquisitions, as well, but also continue to build on the product. I mean, there's, as I say, I mean sources and targets come and go, there's new ones that are created each week, and new adoptions, and so we've got to support those. That's our table stakes, and then continue to make it easier to use, scale even quicker, more autonomous, those type of things. >> And you're working with a lot of big companies, the company's well funded if Crunchbase is up to date, over $50 million in funding. Give us up right there. >> Yeah absolutely, I mean a company is well funded, we're on a good footing. Obviously, it's a very hot space to be in. With COVID this year, like everybody, we sat down and looked in sort of everyone said, "Okay well, let's have a look how "this whole thing's going to shake out, "and get good plan A, B and C in action." And we've sort of ended up with Plan A plus, we've done an annual budget for the year. We had our best quarter ever, and Q2, 193% year over year growth. And it's just, the momentum is just there, I think at large. I mean obviously, it sounds cliche, a lot of people say it around digital transformation and COVID. Absolutely, we've been building this engine for a few years now. And it's really clicked into gear. And I think projects due to COVID and things that would have taken nine, 12 months to happen, they're sort of taking a month or two now. It's been getting driven down from the top. So all of that's come together for us very fortunately, the timing has been ideal. And then tie in something like a Snowflake traction, as you said, we support many other platforms. But all of that together, it just set up really nicely for us, fortunately. >> That's amazing, I mean, with all the turmoil that's going on in the world right now. And all the pain in many businesses. I tell you, I interview people all day every day, and the technology business is really humming. So that's awesome to hear that you guys. I mean, especially if you're in the right place, and data is the place to be. Anthony, thanks so much for coming on theCUBE and summarizing your thoughts, and give us the update on HVR, really interesting. >> Absolutely, I appreciate the time and opportunity. >> Alright, and thank you for watching everybody. This is Dave Vellante for theCUBE, and we'll see you next time. (upbeat music)

>>LA Las Vegas. It's the cube hovering AWS reinvent 2019 brought to you by Amazon web services and along with its ecosystem partners. >>Hey, welcome back to the cube. Our day one coverage of AWS reinvent 19 continues. Lisa Martin with Dave Volante. Dave and I have a couple of guests we'd like you to walk up. We've got Anthony Brooks billions, the CEO of HBR back on the cube. You're alumni. We should get you a pin and snowflake alumni. But Chris, your new Chris Dagon, chief revenue officer from snowflake. Chris, welcome to the program. Excited to be here. All right guys. So even though both companies have been on before, Anthony, let's start with you. Give our audience a refresher about HVR, who you guys are at, what you do. >>Sure. So we're in the data integration space, particularly a real time data integration. So we move data to the cloud in the in the most efficient way and we make sure it's secure and it's accurate and you're moving into environments such as snowflake. Um, and that's where we've got some really good customers that we happy to talk about joint custody that we're doing together. But Chris can tell us a little bit about snowflake. >>Sure. And snowflake is a cloud data warehousing company. We are cloud native, we are on AWS or on GCP and we're on Azure. And if you look at the competitive landscape, we compete with our friends at Amazon. We compete with our friends at Microsoft and our friends at Google. So it's super interesting place to be, but it very exciting at the same time and super excited to partner with Anthony and some others who aren't really a friends. That's correct. So I wonder if we could start by just talking about the data warehouse sort of trends that you guys see. When I talk to practitioners in the old days, they used to say to me things like, Oh, infrastructure management, it's such a nightmare. It's like a snake swallowing a basketball every time until it comes out with a new chips. We chase it because we just need more performance and we can't get our jobs done fast enough. And there's only three. There's three guys that we got to go through to get any answers and it was just never really lived up to the promise of 360 degree view of your business and realtime analytics. How has that changed? >>Well, there's that too. I mean obviously the cloud has had a big difference on that illustrious city. Um, what you would find is in, in, in yesterday, customers have these, a retail customer has these big events twice a year. And so to do an analysis on what's being sold and Casper's transactions, they bought this big data warehouse environment for two events a year typically. And so what's happening that's highly cost, highly costly as we know to maintain and then cause the advances in technology and trips and stuff. And then you move into this cloud world which gives you that Lester city of scale up, scale down as you need to. And then particular where we've got Tonies snowflake that is built for that environment and that elicited city. And so you get someone like us that can move this data at today's scale and volume through these techniques we have into an environment that then bleeds into helping them solve the challenge that you talk about of Yesi of >>these big clunky environments. That side, I think you, I think you kind of nailed it. I think like early days. So our founders are from Oracle and they were building Oracle AI nine nine, 10 G. and when I interviewed them I was the first sales rep showing up and day one I'm like, what the heck am I selling? And when I met them I said, tell me what the benefit of snowflake is. And they're like, well at Oracle, and we'd go talk to customers and they'd say, Oracles, you know, I have this problem with Oracle. They'd say, Hey, that's, you know, seven generations ago were Oracle. Do you have an upgraded to the latest code? So one of the things they talked about as being a service, Hey, we want to make it really easy. You never have to upgrade the service. And then to your point around, you have a fixed amount of resources on premise, so you can't all of a sudden if you have a new project, do you want to bring on the first question I asked when I started snowflake to customers was how long does it take you to kick off a net new workload onto your data, onto your Vertica and it take them nine to 12 months because they'd have to go procure the new hardware, install it, and guess what? >>With snowflake, you can make an instantaneous decision and because of our last test city, because the benefits of our partner from Amazon, you can really grow with your demand of your business. >>Many don't have the luxury of nine to 12 months anymore, Chris, because we all know if, if an enterprise legacy business isn't thinking, there's somebody not far behind me who has the elasticity, who has the appetite, who's who understands the opportunity that cloud provides. If you're not thinking that, as auntie Jessie will say, you're going to be on the wrong end of that equation. But for large enterprises, that's hard. The whole change culture is very hard to do. I'd love to get your perspective, Chris, what you're seeing in terms of industries shifting their mindsets to understand the value that they could unlock with this data, but how are big industries legacy industries changing? >>I'd say that, look, we were chasing Amad, we were chasing the cloud providers early days, so five years ago, we're selling to ad tech and online gaming companies today. What's happened in the industry is, and I'll give you a perfect example, is Ben wa and I, one of our founders went out to one of the largest investment banks on wall street five years ago, and they said, and they have more money than God, and they say, Hey, we love what you've built. We love, when are you going to run on premise? And Ben, Ben wa uttered this phrase of, Hey, you will run on the public cloud before we ever run in the private cloud. And guess what? He was a truth teller because five years later, they are one of our largest customers today. And they made the decision to move to the cloud and we're seeing financial services at a blistering face moved to the cloud. >>And that's where, you know, partnering with folks from HR is super important for us because we don't have the ability to just magically have this data appear in the cloud. And that's where we rely quite heavily on on instance. So Anthony, in the financial services world in particular, it used to be a cloud. Never that was an evil word. Automation. No, we have to have full control and in migration, never digital transformation to start to change those things. It's really become an imperative, but it's by in particular is really challenging. So I wonder if we could dig into that a little bit and help us understand how you solve that problem. >>Yes. A customer say they want to adopt some of these technologies. So there's the migration route. They may want to go adopt some of these, these cloud databases, the cloud data warehouses. And so we have some areas where we, you know, we can do that and keep the business up and running at the same time. So the techniques we use are we reading the transactional logs, other databases or something called CDC. And so there'll be an initial transfer of the bulk of the data initiative stantiating or refresh. At that same time we capturing data out of the transaction logs, wildlife systems live and doing a migration to the new environment or into snowflakes world, capturing data where it's happening, where the data is generated and moving that real time securely, accurately into this environment for somewhere like 1-800-FLOWERS where they can do this, make better decisions to say the cost is better at point of sale. >>So have all their business divisions pulling it in. So there's the migration aspects and then there's the, the use case around the realtime reporting as well. So you're essentially refueling the plane. Well while you're in mid air. Um, yeah, that's a good one. So what does the customer see? How disruptive is it? How do you minimize that disruption? Well, the good thing is, well we've all got these experienced teams like Chris said that have been around the block and a lot of us have done this. What we do, what ed days fail for the last 15 years, that companies like golden gate that we sold to Oracle and those things. And so there's a whole consultative approach to them versus just here's some software, good luck with it. So there's that aspect where there's a lot of planning that goes into that and then through that using our technologies that are well suited to this Appleton shows some good success and that's a key focus for us. And in our world, in this subscription by SAS top world, customer success is key. And so we have to build a lot of that into how we make this successful as well. >>I think it's a barrier to entry, like going, going from on premise to the cloud. That's the number one pushback that we get when we go out and say, Hey, we have a cloud native data warehouse. Like how the heck are we going to get the data to the cloud? And that's where, you know, a partnership with HR. Super important. Yeah. >>What are some of the things that you guys encountered? Because we many businesses live in the multi-cloud world most of the time, not by strategy, right? A lot of the CIO say, well we sort of inherited this, or it's M and a or it's developers that have preference. How do you help customers move data appropriately based on the value that the perceived value that it can give in what is really a multi world today? Chris, we'll start with you. >>Yeah, I think so. So as we go into customers, I think the biggest hurdle for them to move to the cloud is security because they think the cloud is not secure. So if we, if you look at our engagement with customers, we go in and we actually have to sell the value snowflake and then they say, well, okay great, go talk to the security team. And then we talked to security team and say, Hey, let me show you how we secure data. And then then they have to get comfortable around how they're going to actually move, get the data from on premise to the cloud. And that's again, when we engage with partners like her. So yeah, >>and then we go through a whole process with a customer. There's a taking some of that data in a, in a POC type environment and proving that after, as before it gets rolled out. And a lot of, you know, references and case studies around it as well. >>Depends on the customer that you have some customers who are bold and it doesn't matter the size. We have a fortune 100 customer who literally had an on premise Teradata system that they moved from on prem, from on premise 30 to choose snowflake in 111 days because they were all in. You have other customers that say, Hey, I'm going to take it easy. I'm going to workload by workload. And it just depends. And the mileage may vary is what can it give us an example of maybe a customer example or in what workloads they moved? Was it reporting? What other kinds? Yeah. >>Oh yeah. We got a couple of, you mean we could talk a little bit about 1-800-FLOWERS. We can talk about someone like Pitney Bowes where they were moving from Oracle to secret server. It's a bunch of SAP data sitting in SAP ECC. So there's some complexity around how you acquire, how you decode that data, which we ever built a unique ability to do where we can decode the cluster and pool tables coupled with our CDC technique and they had some stringent performance loads, um, that a bunch of the vendors couldn't meet the needs between both our companies. And so we were able to solve their challenge for them jointly and move this data at scale in the performance that they needed out with these articles, secret server enrollments into, into snowflake. >>I almost feel like when you have an SAP environment, it's almost stuck in SAP. So to get it out is like, it's scary, right? And this is where it's super awesome for us to do work like this. >>On that front, I wanted to understand your thoughts on transformation. It's a word, it's a theme of reinvent 2019. It's a word that we hear at every event, whether we're talking about digital transformation, workforce, it, et cetera. But one of the things that Andy Jassy said this morning was that got us start. It's this is more than technology, right? This, the next gen cloud is more than technology. It's about getting those senior leaders on board. Chris, your perspective, looking at financial services first, we were really surprised at how quickly they've been able to move. Understanding presumably that if they don't, there's going to be other businesses. But are you seeing that as the chief revenue officer or your conversations starting at that CEO level? >>It kinda has to like in the reason why if you do in bottoms up approach and say, Hey, I've got a great technology and you sell this great technology to, you know, a tech person. The reality is unless the C E O CIO or CTO has an initiative to do digital transformation and move to the cloud, you'll die. You'll die in security, you'll die in legal lawyers love to kill deals. And so those are the two areas that I see D deals, you know, slow down significantly. And that's where, you know, we, it's, it's getting through those processes and finding the champion at the CEO level, CIO level, CTO level. If you're, if you're a modern day CIO and you do not have a a cloud strategy, you're probably going to get replaced >>in 18 months. So you know, you better get on board and you'd better take, you know, taking advantage of what's happening in the industry. >>And I think that coupled with the fact that in today's world, you mean, you said there's a, it gets thrown around as a, as a theme and particularly the last couple of years, I think it's, it's now it is actually a strategy and, and reality because what Josephine is that there's as many it tech savvy people sit in the business side of organizations today that used to sit in legacy it. And I think it's that coupled with the leadership driving it that's, that's demanding it, that demanding to be able to access that certain type of data in a geo to make decisions that affect the business. Right now. >>I wonder if we could talk a little bit more about some of the innovations that are coming up. I mean I've been really hard on data. The data warehouse industry, you can tell I'm jaded. I've been around a long time. I mean I've always said that that Sarbanes Oxley saved the old school BI and data warehousing and because all the reporting requirements, and again that business never lived up to its promises, but it seems like there's this whole new set of workloads emerging in the cloud where you take a data warehouse like a snowflake, you may be bringing in some ML tools, maybe it's Databricks or whatever. You HVR helping you sort of virtualize the data and people are driving new workloads that are, that are bringing insights that they couldn't get before in near real time. What are you seeing in terms of some of those gestalt trends and how are companies taking advantage of these innovations? >>I think one is just the general proliferation of data. There's just more data and like you're saying from many different sources, so they're capturing data from CNC machines in factories, you know like like we do for someone like GE, that type of data is to data financial data that's sitting in a BU taking all of that and going there's just as boss some of data, how can we get a total view of our business and at a board level make better decisions and that's where they got put it in I snowflake in this an elastic environment that allows them to do this consolidated view of that whole organization, but I think it's largely been driven by things that digitize their sensors on everything and there's just a sheer volume of data. I think all of that coming together is what's, what's driven it >>is is data access. We talked about security a little bit, but who has rights to access the data? Is that a challenge? How are you guys solving that or is it, I mean I think it's like anything like once people start to understand how a date where we're an acid compliant date sequel database, so we whatever your security you use on your on premise, you can use the same on snowflake. It's just a misperception that the industry has that being on, on in a data center is more secure than being in the cloud and it's actually wrong. I guess my question is not so much security in the cloud, it's more what you were saying about the disparate data sources that coming in hard and fast now. And how do you keep track of who has access to the data? I mean is it another security tool or is it a partnership within owes? >>Yeah, absolutely man. So there's also, there's in financial data, there's certain geos, data leaves, certain geos, whether it be in the EU or certain companies, particularly this end, there's big banks now California, there's stuff that we can do from a security perspective in the data that we move that's secure, it's encrypted. If we capturing data from multiple different sources, items we have that we have the ability to take it all through one, one proxy in the firewall, which does, it helps him a lot in that aspect. Something unique in our technology. But then there's other tools that they have and largely you sit down with them and it's their sort of governance that they have in the, in the organization to go, how do they tackle that and the rules they set around it, you know? >>Well, last question I have is, so we're seeing, you know, I look at the spending data and my breaking analysis, go on my LinkedIn, you'll see it snowflakes off the charts. It's up there with, with robotic process automation and obviously Redshift. Very strong. Do you see those two? I think you addressed it before, but I'd love to get you on record sort of coexisting and thriving. Really, that's not the enemy, right? It's the, it's the Terra data's and the IBM's and the Oracles. The, >>I think, look, uh, you know, Amazon, our relationship with Amazon is like a, you know, a 20 year marriage, right? Sometimes there's good days, sometimes there's bad days. And I think, uh, you know, every year about this time, you know, we get a bat phone call from someone at Amazon saying, Hey, you know, the Redshift team's coming out with a snowflake killer. And I've heard that literally for six years now. Um, it turns out that there's an opportunity for us to coexist. Turns out there's an opportunity for us to compete. Um, and it's all about how they handle themselves as a business. Amazon has been tremendous in separation of that, of, okay, are going to partner here, we're going to compete here, and we're okay if you guys beat us. And, and so that's how they operate. But yes, it is complex and it's, it's, there are challenges. >>Well, the marketplace guys must love you though because you're selling a lot of computers. >>Well, yeah, yeah. This is three guys. They, when they left, we have a summer thing. You mean NWS have a technological DMS, their data migration service, they work with us. They refer opportunities to us when it's these big enterprises that are use cases, scale complexity, volume of data. That's what we do. We're not necessary into the the smaller mom and pop type shops that just want to adopt it, and I think that's where we all both able to go coexist together. There's more than enough. >>All right. You're right. It's like, it's like, Hey, we have champions in the Esri group, the EEC tuna group, that private link group, you know, across all the Amazon products. So there's a lot of friends of ours. Yeah, the red shift team doesn't like us, but that's okay. I can live in >>healthy coopertition, but it just goes to show that not only do customers and partners have toys, but they're exercising it. Gentlemen, thank you for joining David knee on the key of this afternoon. We appreciate your time. Thank you for having us. Pleasure our pleasure for Dave Volante. I'm Lisa Martin. You're watching the queue from day one of our coverage of AWS reinvent 19 thanks for watching.

>> Live from Las Vegas, it's theCUBE covering AWS re:Invent 2018. Brought to you by Amazon Web Services, Intel, and their ecosystem partners. >> Well, good afternoon, or good evening, if you're watching us back on the East Coast right now. We are live here at AWS re:Invent in Las Vegas along with Justin Warren, I'm John Walls. We're now joined by Anthony Brooks-Williams, who's the CEO of HVR. >> Thanks for having me here today. >> Thanks for being here with us today, and Paul Specketer, who's the Enterprise Data Architect at SUEZ. Paul, good afternoon to you. >> Good afternoon. >> All right so let's just first off, tell us a little bit about your respective companies and why you're here together, and why you're here at the show? Anthony if you will. >> Sure absolutely. So at HVR, we provide the most efficient way for companies to move their data, in particular to the cloud and at scale, and that they have the peace of mind that when they move their data that it's accurate, and we give them insights on the data that we move. So we do that for companies such as SUEZ, enable them to get their data into S3, into Redshift, and so they can make decisions on the freshest data. >> All right, go on Paul. >> So yeah, we're formally GE, and SUEZ acquired our company. So now we're standing up an entire data platform, all the applications are coming over to AWS. So in the past year, we've had to stand up for Redshift cluster, the full ETL backbone behind that, and including the replication from our ERP system into that environment. So we're going live with that in the next coming months. So that's why we're here. We use HVR to move our data around before the ETL process. >> Anthony, you mentioned that if customers want to make decisions on the latest, the freshest data. >> Yeah. >> So what are the kinds of analysis, and what are the kinds of the decisions that customers are trying to make here? >> Sure, obviously it depends on the customer themselves. >> Clearly yeah. >> If it's a big e-commerce vendor, someone like that or where are certain products selling at a certain region based on a certain weather pattern or something like that. Our ability to capture that at a store level, and moving that back so they know how to fulfill the warehouses or what stock is out there, that enables them to run a more profitable business. Whether it be someone like that or Paul's previous company, someone like GE from an aviation perspective to transportation. It's what's happening in the environment in the systems. So giving them the ability to move that data, move it at volume, and just make good business decisions. Even the main use case for us is consolidated reporting. Consolidated reporting along some of those financials as well. So the exact level, board level are making decisions on their business with the freshest numbers that are sitting in front of them at that time. >> Paul, what are some of the key ways that HVR will be able to help you in designing that system that can support the needs of those customers? What are some of the key things where you've got there is when actually we really the help of someone like HVR to help us to do that. >> Long ago, we had database triggers, and we had some programs that we had to write to capture changes. That all goes away when you do log based data replication. So for us, we changed that whole strategy and we said, you know what, just take everything from the ERP. Move it up into the cloud. Then from there, move it where you need to, process the ETL, and shift it around. So for us, it's just the first goal is take everything as is, get it up into the cloud as the replicated data set. Then from there, we do our ETL processing. We watch that, or we view that in Tableau. So for us, what I'm building allows us to close our books in one to two days. As when we were in GE, we're driving towards a one-day close. Now that we're in SUEZ, we're doing a hard close every month. So we're trying to drive that time down as low as possible. You've got people sitting around waiting for the report to look right. So the more we can do to drive that time down, the more people get their weekends back. >> Right, right, yeah. >> People like weekends. >> Yeah all right, so you talked about accuracy. >> Yep >> You talked about volume. >> Yep. >> All right, so obviously you got a lot more data coming through. >> Yeah. >> The need to keep it a mart. What about speed and latency? I mean how of a concern is that for you? Because you got this bigger funnel that you need all the time. >> Absolutely, and especially in today's world of the cloud, and moving data across wide area networks. So that's whereby the technique that we use, the CDC, the change data capture, where you're reading those transaction logs. You're only capturing the changes, and moving those across the network. Then our technology, we have some proprietary techniques we do around compression that further magnifies that bandwidth. So you're magnifying the bandwidth. You're able to move a large volume of data more efficiently, and the latency certainly comes in to them as well. So built into the product, we have a feature around the data accuracy perspective. So that no matter what the source or target system is, they know their data is absolutely accurate. And then tied to that is a product that we released recently is around insights. That's telling them the statistics on the data that we're moving. We've gathered that and now we now showing that published into the customers, largely because customers like Paul, that were doing this themselves, we provided the statistics on the data, and they were having a front-end on top of that. We've not taken that to the broader market. So that's showing them exactly things like latency. So they'll be able to drill in, and go that graph or that line is red or it's thicker, and it's telling them the latency. We should probably do something about that. What's the bottleneck there? So it's all coming together now. Particularly in this cloudy world of moving this data. >> So Paul, can you give us an example then of what Anthony has just talked about? How in real life, of how this happened for you that with that kind of reporting, that you saw whatever hiccup there was in the system if you will, that it identified that and solved that problem for you. >> As far as the short cycle close, I had a hard time hearing you actually. >> Yeah, from the statistics, I was talking about when we were moving the data, and how you were collecting stats on that data that moved already, that's enabled you, particularly from a latency perspective of the volume you can move. If there's an issue with it, what do you do with that. >> So one of the challenges we always have was when you go through a long cycle replication, and you've been doing it for months, and I ask you the question. Are you sure you got every change? Do you know? So that's we never know but now with increases in the Redshift cluster performance, with the DC2 clusters, increases in the performance of Redshift or of HVR in moving that data in, our strategy now is to not doubt the data ever. We just refresh it every month, right before it close. We refresh the data. It takes us like four hours to move two terabytes into Redshift. So why not? That changes your approach when you don't have to stress out about the data being accurate, week in, week out. Every quarter right before it closes, you're getting a fresh copy. So that really changed my life. It's being able to know going into close, before the finance guys look at it, that the data is perfect. >> So now that you've had that issue or that concern taken away, and you don't have to worry about it anymore, has that open up new possibilities in like I can now attempt to do these things, which I would have loved to, like I thought about it, but like I don't have time. We have these other constraints. So with those constraints gone, what are you now able to do? >> What we're going to look at now is instead of doing ETL inside of the Redshift cluster, we're going to take that out. Because we actually do about three quarter of the space in our cluster is used for ETL. So we're going to carve that out, maybe do it in S3, we're not sure. As soon as we do that, we'll be down to like a four-node Redshift cluster. That'll save a lot of money. So that for us-- >> Big savings. >> Yeah. >> Now that we're in the cloud, the next push is how do we optimize it? How do we take advantages of cloud native services that we never had access to before? >> Right. >> Yeah. >> So that's what's on my horizon. It's looking at that and saying what can I do in the next year? >> All right, we're seeing massive growth in data across. We've had many conversations so far today about data being generated from IoT devices at the edge. We're having to process it in more places because we're just physically moving this data around. It's such a huge problem. It's why you exist. >> Yep. >> So what do you see customers deal? When they're trying to deal with this issue, this data is not going to get smaller. There's going to be more and more of this data. So how are you helping customers to grapple with this issue about well, where should we move the data? Should we move all of it into the cloud? Is that the only direction that it should be moved? Or you're able to help them say, you know what we want to move some of it to here. We'll place some other data over there. We can help you move it around no matter where it needs to go. >> Certainly, so we're obviously agnostic to where they want to move their data. Well, given the years of experience that we have, and the people we have in the company, we certainly are able to lend that seasoned advice to them of where we think an efficient place will be to move that data. Certainly within the technology of HVR, it's very efficient at capturing data once, and then sending it to many. >> Right. >> That's how we really set ourselves apart from a complexity of we're being modular and flexible of capturing that data, feed on the cusp where they need to. We can send to capture one, send to multiple target systems. So they could go and say, I'm going to put the bulk of this feed into S3. I'm going to take a bit of that, and put it into Redshift. So it gives them that flexibility to do that. So obviously with us, some of our skilled architects that we have in the field, are able to make them, not just go and sell a product, actually help them with a solution. We're out there selling software but we're making sure that we're delivering customers with a total solution. Because I think we look back on yesteryear, and some of the data lags, you know the stats from Gartner, 70% of those projects failed. It was just, I'm going to take it all and put it in there. Well why, how? I think it's planning those well together, and sort of the defacto data lag we've seen out today is seven out of 10 times in something like S3. So take the architecture, take the technology, take the people, and help them go execute on their plan, and just lend some of that advice along the process to them. >> That sounds like something that would add a lot of value. >> Yeah. >> You put it there because you could. >> Absolutely. >> That's why. >> Why, 'cause we can. >> It was a small improvement, it was a good place to put it. >> It fits. >> I might not look at it for a long time. >> It was cheap, and it was clear. >> Put it on top there, right. >> Absolutely. >> Gentlemen, thank you for being with us. We appreciate the time. >> Thank you for the time. >> Paul, we're really happy you have your weekends back. Thank goodness on that, excellent. >> Absolutely. >> Thank you. >> Back with more, here from AWS re:Invent, pardon me, from Las Vegas. We're live at the Sands. We'll wrap up in just a moment. (enlightening music)

(upbeat music) >> Hey, gang. Welcome back to theCUBE's coverage of Snowflake Summit '22 live on the show floor at Caesar's Forum in Las Vegas. Lisa Martin here with Dave Vellante. Couple of guests joining us to unpack more of what we've been talking about today. George Fraser joins us, the CEO of Fivetran, and Veronika Durgin, the head of data at Saks Fifth Avenue. Guys, welcome to the program. >> Thank you for having us. >> Hello. >> George, talk to us about Fivetran for the audience that may not be super familiar. Talk to us about the company, your vision, your mission, your differentiation, and then maybe the partnership with Snowflake. >> Well, a lot of people in the audience here at Snowflake Summit probably are familiar with Fivetran. We have almost 2000 shared customers with them. So a considerable amount of the data that we're all talking about here, flows through Fivetran. But in brief, what Fivetran is, is we're data pipeline. And that means that we go get all the data of your company in all the places that it lives. So all your tools and systems that you use to run your company. We go get that data and we bring it all together in one place like Snowflake. And that is the first step in doing anything with data is getting it all in one place. >> So you've been considerable amount of shared customers. I think I saw this morning on the slide over 5,900, but you're saying you're already at around 2000 shared customers. Lots of innovation I'm sure, with between both companies, but talk to us about some of the latest developments at Fivetran, in terms of product, in terms of company growth, what's going on? >> Well, one of the biggest things that happened recently with Fivetran is we acquired another data integration company called HVR. And HVR specialty has always been replicating the biggest, baddest enterprise databases like Oracle and SQL Server databases that are enormous, that are run within an inch of their capabilities by their DBAs. And HVR was always known as the best in the business at that scenario. And by bringing that together with Fivetran, we now really have the full spectrum of capabilities. We can replicate all types of data for all sizes of company. And so that's a really exciting development for us and for the industry. >> So Veronika, head of data at Saks, what does that entail? How do you spend your time? What's your purview? >> So the cool thing abouts Saks is a very old company. Saks is the premier luxury e-commerce platform. And we help our Saks Fifth Avenue customers just express themselves through fashion. So we're trying to modernize very old company and we do have the biggest, baddest databases of any flavor you can imagine. So my job is to modernize, to bring us to near real-time data, to make sure data is available to all of our users so they can actually take advantage of it. >> So let's talk about some of those biggest, baddest hair balls that you've, and how you deal with that. So lot of over time, you've built up a lot of data. You've got different data stores. So, what are you doing with that? And what role does Fivetran and Snowflake play in helping you modernize? >> Yeah, Fivetran helps us ingest data from all of those data sources into Snowflake near real-time. It's very important to us. And like one of the examples that I give is within a matter of maybe a few weeks, we were able to get data from over a dozen of different data sources into Snowflake in near real-time. And some of those data sources were not available to our users in the past, and everybody was so excited. And the reason they weren't available is because they require a lot of engineering effort to actually build those data pipelines to manage them and maintain them. >> Lisa: Whoa, sorry. >> That was just a follow up. So, Fivetran is the consolidator of all that data and- >> That's right. >> Snowflake plays that role also. >> We bring it all together, and the place that it is consolidated is Snowflake. And from there you can really do anything with it. And there's really three things you were touching on it that make data integration hard. One is volume, and that's the one that people tend to talk about, just size of data. And that is important, but it's not the only thing. It's also latency. How fresh is the data in the locus of consolidation? Before Fivetran, the state of the art was nightly snapshots, once a day was considered pretty good. And we consider now once a minute pretty good and we're trying to make it even better. And then the last challenge, which people tend not to talk about, it's the dark secret of our industry is just incidental complexity. All of these data sources have a lot of strange behaviors and rules and corner cases. Every data source is a little bit different. And so a lot of what we bring that to the table, is that we've done the work over 10 years. And in the case of HVR, since the 90s', to map out all of these little complexities of all these data sources, that as a user, you don't have to see it. You just connect source, connect destination, and that's it. >> So you don't have to do the M word migrate off of all those databases. You can maybe allow them to dial them down over time, then create new value with using Fivetran and Snowflake. Is that the right way to think about it? >> Well, Fivetran, it's incredibly simple. You just connect it to whatever source, And then the matter of minutes you have a pipeline. And for us, it's in the matter of minutes, for Fivetran, there's hundreds of engineers, we're extending our data engineering team to now Fivetran. And we can pick and choose which tables we want to replicate which fields. And once data lands in Snowflake, now we have data across different sources in one place, in central place. And now we can do all kinds of different things. We can integrate it data together, we can do validations, we can do reconciliations. We now have ability to do point in time historical journey, in the past in transactional system, you don't see that, you only see data that's right now, but now that we replicate everything to Snowflake and Snowflake being so powerful as an analytical platform, we can do, what did it look like two months ago? What did it look like two years ago? >> You've got all that time series data, okay. >> And to address that word you mentioned a moment ago, migrate, this is something people often get confused about. What we're talking about here is not a migration, these source systems are not going away. These databases are the systems powering saks.com and they're staying right there. They're the systems you interact with when you place an order on this site. The purpose of our tool and the whole stack that Veronika has put together, is to serve other workloads in Snowflake that need to have access to all of the data together. >> But if you didn't have Snowflake, you would have to push those other data stores, try to have them do things that they have sometimes a tough time doing. >> Yeah, and you can't run analytical workloads. You cannot do reporting on the transactional database. It's not meant for that. It's supporting capability of an application and it's configured to be optimized for that. So we always had to offload those specific analytical reporting functionality, or machine learning somewhere else, and Snowflake is excellent for that. It's meant for that, yeah. >> I was going to ask you what you were doing before, you just answered that. What was the aha moment for realizing you needed to work with the power of Fivetran and Snowflake? If we look at, you talked about Saks being a legacy history company that's obviously been very successful at transforming to the digital age, but what was that one thing, as the head of the data you felt this is it? >> Great question. I've worked with Fivetran in the past. This is my third company, same with Snowflake. I actually brought Fivetran into two companies at this point. So my first experience with both Fivetran and Snowflake, was this like, this is where I want to be, this is the stack and the tooling, and just the engineering behind it. So as I moved on the next company, that that was, I'm bringing tools with me. So that was part. And the other thing I wanted to mention, when we evaluate tools for a new platform, we look at things in like three dimensions, right? One with cloud first, we want to have cloud native tools, and they have to be modular, but we also don't want to have too many tools. So Fivetran's certainly checks that off. They're first cloud native, and they also have a very long list of connectors. The other thing is for us, it's very important that data engineering effort is spent on actually analyzing data, not building pipelines and supporting infrastructure. In Fivetran, reliable, it's secure, it has various connectors, so it checks off that box as well. And another thing is that we're looking for companies we can partner with. So companies that help us grow and grow with us, we'll look in a company culture, their maturity, how they treat their customers and how they innovate. And again, Fivetran checks off that box as well. >> And I imagine Snowflake does as well, Frank Lutman on stage this morning talked about mission alignment. And it seemed to me like, wow, one of the missions of Snowflake is to align with its customer's missions. It sounds like from the conversations that Dave and I have had today, that it's the same with partners, but it sounds like you have that cultural alignment with Fivetran and Snowflake. >> Oh, absolutely. >> And Fivetran has that, obviously with 2000 shared customers. >> Yeah, I think that, well, not quite there yet, but we're close, (laughs) I think that the most important way that we've always been aligned with our customers is that we've been very clear on what we do and don't do. And that our job is to get the data from here to there, that the data be accurately replicated, which means in practice often joke that it is exactly as messed up as it was in the source. No better and no worse, but we really will accomplish that task. You do not need to worry about that. You can well and fully delegate it to us, but then what you do with the data, we don't claim that we're going to solve that problem for you. That's up to you. And anyone who claims that they're going to solve that problem for you, you should be very skeptical. >> So how do you solve that problem? >> Well, that's where modeling comes in, right? You get data from point A to point B, and it's like bad in, bad out. Like, that's it, and that's where we do those reconciliations, and that's where we model our data. We actually try to understand what our businesses, how our users, how they talk about data, how they talk about business. And that's where data warehouse is important. And in our case, it's data evolve. >> Talk to me a little bit before we wrap here about the benefits to the end user, the consumer. Say I'm on saks.com, I'm looking for a particular item. What is it about this foundation that Saks has built with Fivetran and with Snowflake, that's empowering me as a consumer, to be able to get, find what I want, get the transaction done like that? >> So getting access to, our end goal is to help our customers, right? Make their experience beautiful, luxurious. We want to make sure that what we put in front of you is what you're looking for. So you can actually make that purchase, and you're happy with it. So having that data, having that data coming from various different sources into one place enables us to do that near real-time analytics so we can help you as a customer to find what you're looking for. >> Magic on the back end, delighting customers. >> So the world is still messed up, right? Airlines are out of whack. There's supply imbalances. You've got the situation in Ukraine with oil prices. The Fed missed the mark. So can data solve these problems? If you think about the context of the macro environment, and you bring it down to what you're seeing at Saks, with your relationship with Fivetran and with Snowflake, do you see the light at the end of that confusion tunnel? >> That's such a great question. Very philosophical. I don't think data can solve it. Is the people looking at data and working together that can solve it. >> I think data can help, data can't stop a war. Data can help you forecast supply chain misses and mitigate those problems. So data can help. >> Can be a facilitator. >> Sorry, what? >> Can be a facilitator. >> Yeah, it can be a facilitator of whatever you end up doing with it. Data can be used for good or evil. It's ultimately up to the user. >> It's a tool, right? Do you bring a hammer to a gunfight? No, but t's a tool in the right hands, for the right purpose, it can definitely help. >> So you have this great foundation, you're able to delight customers as especially from a luxury brand perspective. I imagine that luxury customers have high expectations. What's next for Saks from a data perspective? >> Well, we want to first and foremost to modernize our data platform. We want to make sure we actually bring that near real-time data to our customers. We want to make sure data's reliable. That well understood that we do the data engineering and the modeling behind the scenes so that people that are using our data can rely on it. Because it's like, there is bad data is bad data but we want to make sure it's very clear. And what's next? The sky's the limit. >> Can you describe your data teams? Is it highly centralized? What's your philosophy in terms of the architecture of the organization? >> So right now we are starting with a centralized team. It just works for us as we're trying to rebuild our platform, and modernize it. But as we become more mature, we establish our practices, our data governance, our definitions, then I see a future where we like decentralize a little bit and actually each team has their own analytical function, or potentially data engineering function as well. >> That'll be an interesting discussion when you get there. >> That's a hot topic. >> It's one of the hardest problems in building a data team is whether decentralized or decentralized. We're still centralized at Fivetran, but companies now over 1000 people, and we're starting to feel the strain of that. And inevitably, you eventually have to find a way to find scenes and create specialization. >> You just have to be fluid, right? And then go with the company as the company grows and things change. >> Yeah, I've worked with some companies. JPMC is here, they've got a little, I'll call it a skunk works. They're probably under states what they're doing, but they're testing that out. A company like HelloFresh is doing some things 'cause their Hadoop cluster just couldn't scale. So they have to begin to decentralize. It is a hot topic these days. And I'm not sure there's a right or wrong. It's really a situational. But I think in a lot of situations, it's maybe the trend. >> Yeah. >> Yeah, I think centralized versus decentralized technology is a different question than centralized versus decentralized teams. >> Yes. >> They're both valid, but they're very different. And sometimes people conflate them, and that's very dangerous. Because you might want one to be centralized and the other to be decentralized. >> Well, it's true. And I think a lot of folks look at a centralized team and say, "Hey, it's more efficient to have these specialized roles, but at the same time, what's the outcome?" If the outcome can be optimized and it's maybe a little bit more people expensive, or I don't know. And they're in the lines of business where there's data context, that might be a better solution for a company. >> So to truly understand the value of data, you have to specialize in that specific area. So I see people like deep diving into specific vertical or whatever that is, and truly understanding what data they have and how to taken advantage of it. >> Well, all this talk about monetization and building data products, you're there, right? >> Yeah. >> You're on the cusp of that. And so who's going to build those data products? It's going to be somebody in the business. Today they don't "Own the life cycle" of the data. They don't feel responsible for it, but they complain when it's not what they want. And so, I feel as though what Snowflake is doing is actually attacking some of those problems. Not 100% there obviously, but a lot of work to do. >> Great analysts are great navigators of organizations amongst other things. And one of the best things that's happened as part of this evolution from technology like Hadoop to technology like Snowflake is the new stack is a lot simpler. There's a lot less technical knowledge that you need. You still need technical knowledge, but not nearly what you used to. And that has made it accessible to more people. People who bring different skills to the table. And in many cases, those are the skills you really need to deliver value from data is not, do you know the inner workings of HDFS? But do you know how to extract from your constituents in the organization, a precise version of the question that they're trying to ask? >> We really want them spending their time, the technical infrastructure is an operational detail, so you can put your teams on those types of questions, not how do we make it work? And that's what Hadoop was, "Hey, we got it to work." >> And that's something we're obsessed with. We're always trying to hide the technical complexities of the problem of data centralization behind the scenes. Even if it's harder for us, even if it's more expensive for us, we will pay any costs so that you don't have to see it. Because that allows our customers to focus on more high impact. >> Well, this is a case where a technology vendor's R&D is making your life easier. >> Veronika: Easier, right. >> I would presume you'd rather spend money to save time, than spend your time, to save engineering time, to save money. >> That's true. And at the end of the day, hiring three data engineers to do custom work that a tool does, it's actually not saving money. It costs more in the end. But to your point, pulling business people into those data teams gives them ownership, and they feel like they're part of the solution. And it's such a great feeling so that they're excited to contribute, they're excited to help us. So I love where the industry's going like in that direction. >> And of course, that's the theme of the show, the world around data collaborations. Absolutely critical, guys. Thank you so much for joining Dave and me, talking about Fivetran, Snowflake together, what you're doing to empower Saks, to be a data company. I'm going to absolutely have a different perspective next time I shop there. Thanks for joining us. Thank you. >> Dave: Thank you, guys. >> Thank you. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE live from Snowflake Summit '22, from Vegas. Stick around, our next guest joins us momentarily. (upbeat music)

>> Narrator: From around the Globe, it's theCube with digital coverage of Enterprise Data Automation, and event series brought to you by Io-Tahoe. >> Everybody, we're back. And this is Dave Vellante, and we're covering the whole notion of Automated Data in the Enterprise. And I'm really excited to have Paula D'Amico here. Senior Vice President of Enterprise Data Architecture at Webster Bank. Paula, good to see you. Thanks for coming on. >> Hi, nice to see you, too. >> Let's start with Webster bank. You guys are kind of a regional I think New York, New England, believe it's headquartered out of Connecticut. But tell us a little bit about the bank. >> Webster bank is regional Boston, Connecticut, and New York. Very focused on in Westchester and Fairfield County. They are a really highly rated regional bank for this area. They hold quite a few awards for the area for being supportive for the community, and are really moving forward technology wise, they really want to be a data driven bank, and they want to move into a more robust group. >> We got a lot to talk about. So data driven is an interesting topic and your role as Data Architecture, is really Senior Vice President Data Architecture. So you got a big responsibility as it relates to kind of transitioning to this digital data driven bank but tell us a little bit about your role in your Organization. >> Currently, today, we have a small group that is just working toward moving into a more futuristic, more data driven data warehousing. That's our first item. And then the other item is to drive new revenue by anticipating what customers do, when they go to the bank or when they log in to their account, to be able to give them the best offer. And the only way to do that is you have timely, accurate, complete data on the customer and what's really a great value on offer something to offer that, or a new product, or to help them continue to grow their savings, or do and grow their investments. >> Okay, and I really want to get into that. But before we do, and I know you're, sort of partway through your journey, you got a lot to do. But I want to ask you about Covid, how you guys handling that? You had the government coming down and small business loans and PPP, and huge volume of business and sort of data was at the heart of that. How did you manage through that? >> We were extremely successful, because we have a big, dedicated team that understands where their data is and was able to switch much faster than a larger bank, to be able to offer the PPP Long's out to our customers within lightning speed. And part of that was is we adapted to Salesforce very for we've had Salesforce in house for over 15 years. Pretty much that was the driving vehicle to get our PPP loans in, and then developing logic quickly, but it was a 24 seven development role and get the data moving on helping our customers fill out the forms. And a lot of that was manual, but it was a large community effort. >> Think about that too. The volume was probably much higher than the volume of loans to small businesses that you're used to granting and then also the initial guidelines were very opaque. You really didn't know what the rules were, but you were expected to enforce them. And then finally, you got more clarity. So you had to essentially code that logic into the system in real time. >> I wasn't directly involved, but part of my data movement team was, and we had to change the logic overnight. So it was on a Friday night it was released, we pushed our first set of loans through, and then the logic changed from coming from the government, it changed and we had to redevelop our data movement pieces again, and we design them and send them back through. So it was definitely kind of scary, but we were completely successful. We hit a very high peak. Again, I don't know the exact number but it was in the thousands of loans, from little loans to very large loans and not one customer who applied did not get what they needed for, that was the right process and filled out the right amount. >> Well, that is an amazing story and really great support for the region, your Connecticut, the Boston area. So that's fantastic. I want to get into the rest of your story now. Let's start with some of the business drivers in banking. I mean, obviously online. A lot of people have sort of joked that many of the older people, who kind of shunned online banking would love to go into the branch and see their friendly teller had no choice, during this pandemic, to go to online. So that's obviously a big trend you mentioned, the data driven data warehouse, I want to understand that, but what at the top level, what are some of the key business drivers that are catalyzing your desire for change? >> The ability to give a customer, what they need at the time when they need it. And what I mean by that is that we have customer interactions in multiple ways. And I want to be able for the customer to walk into a bank or online and see the same format, and being able to have the same feel the same love, and also to be able to offer them the next best offer for them. But they're if they want looking for a new mortgage or looking to refinance, or whatever it is that they have that data, we have the data and that they feel comfortable using it. And that's an untethered banker. Attitude is, whatever my banker is holding and whatever the person is holding in their phone, that is the same and it's comfortable. So they don't feel that they've walked into the bank and they have to do fill out different paperwork compared to filling out paperwork on just doing it on their phone. >> You actually do want the experience to be better. And it is in many cases. Now you weren't able to do this with your existing I guess mainframe based Enterprise Data Warehouses. Is that right? Maybe talk about that a little bit? >> Yeah, we were definitely able to do it with what we have today the technology we're using. But one of the issues is that it's not timely. And you need a timely process to be able to get the customers to understand what's happening. You need a timely process so we can enhance our risk management. We can apply for fraud issues and things like that. >> Yeah, so you're trying to get more real time. The traditional EDW. It's sort of a science project. There's a few experts that know how to get it. You can so line up, the demand is tremendous. And then oftentimes by the time you get the answer, it's outdated. So you're trying to address that problem. So part of it is really the cycle time the end to end cycle time that you're progressing. And then there's, if I understand it residual benefits that are pretty substantial from a revenue opportunity, other offers that you can make to the right customer, that you maybe know, through your data, is that right? >> Exactly. It's drive new customers to new opportunities. It's enhanced the risk, and it's to optimize the banking process, and then obviously, to create new business. And the only way we're going to be able to do that is if we have the ability to look at the data right when the customer walks in the door or right when they open up their app. And by doing creating more to New York times near real time data, or the data warehouse team that's giving the lines of business the ability to work on the next best offer for that customer as well. >> But Paula, we're inundated with data sources these days. Are there other data sources that maybe had access to before, but perhaps the backlog of ingesting and cleaning in cataloging and analyzing maybe the backlog was so great that you couldn't perhaps tap some of those data sources. Do you see the potential to increase the data sources and hence the quality of the data or is that sort of premature? >> Oh, no. Exactly. Right. So right now, we ingest a lot of flat files and from our mainframe type of front end system, that we've had for quite a few years. But now that we're moving to the cloud and off-prem and on-prem, moving off-prem, into like an S3 Bucket, where that data we can process that data and get that data faster by using real time tools to move that data into a place where, like snowflake could utilize that data, or we can give it out to our market. Right now we're about we do work in batch mode still. So we're doing 24 hours. >> Okay. So when I think about the data pipeline, and the people involved, maybe you could talk a little bit about the organization. You've got, I don't know, if you have data scientists or statisticians, I'm sure you do. You got data architects, data engineers, quality engineers, developers, etc. And oftentimes, practitioners like yourself, will stress about, hey, the data is in silos. The data quality is not where we want it to be. We have to manually categorize the data. These are all sort of common data pipeline problems, if you will. Sometimes we use the term data Ops, which is sort of a play on DevOps applied to the data pipeline. Can you just sort of describe your situation in that context? >> Yeah, so we have a very large data ops team. And everyone that who is working on the data part of Webster's Bank, has been there 13 to 14 years. So they get the data, they understand it, they understand the lines of business. So it's right now. We could the we have data quality issues, just like everybody else does. But we have places in them where that gets cleansed. And we're moving toward and there was very much siloed data. The data scientists are out in the lines of business right now, which is great, because I think that's where data science belongs, we should give them and that's what we're working towards now is giving them more self service, giving them the ability to access the data in a more robust way. And it's a single source of truth. So they're not pulling the data down into their own, like Tableau dashboards, and then pushing the data back out. So they're going to more not, I don't want to say, a central repository, but a more of a robust repository, that's controlled across multiple avenues, where multiple lines of business can access that data. Is that help? >> Got it, Yes. And I think that one of the key things that I'm taking away from your last comment, is the cultural aspects of this by having the data scientists in the line of business, the lines of business will feel ownership of that data as opposed to pointing fingers criticizing the data quality. They really own that that problem, as opposed to saying, well, it's Paula's problem. >> Well, I have my problem is I have data engineers, data architects, database administrators, traditional data reporting people. And because some customers that I have that are business customers lines of business, they want to just subscribe to a report, they don't want to go out and do any data science work. And we still have to provide that. So we still want to provide them some kind of regiment that they wake up in the morning, and they open up their email, and there's the report that they subscribe to, which is great, and it works out really well. And one of the things is why we purchased Io-Tahoe was, I would have the ability to give the lines of business, the ability to do search within the data. And we'll read the data flows and data redundancy and things like that, and help me clean up the data. And also, to give it to the data analysts who say, all right, they just asked me they want this certain report. And it used to take okay, four weeks we're going to go and we're going to look at the data and then we'll come back and tell you what we can do. But now with Io-Tahoe, they're able to look at the data, and then in one or two days, they'll be able to go back and say, Yes, we have the data, this is where it is. This is where we found it. This is the data flows that we found also, which is what I call it, is the break of a column. It's where the column was created, and where it went to live as a teenager. (laughs) And then it went to die, where we archive it. And, yeah, it's this cycle of life for a column. And Io-Tahoe helps us do that. And we do data lineage is done all the time. And it's just takes a very long time and that's why we're using something that has AI in it and machine running. It's accurate, it does it the same way over and over again. If an analyst leaves, you're able to utilize something like Io-Tahoe to be able to do that work for you. Is that help? >> Yeah, so got it. So a couple things there, in researching Io-Tahoe, it seems like one of the strengths of their platform is the ability to visualize data, the data structure and actually dig into it, but also see it. And that speeds things up and gives everybody additional confidence. And then the other piece is essentially infusing AI or machine intelligence into the data pipeline, is really how you're attacking automation. And you're saying it repeatable, and then that helps the data quality and you have this virtual cycle. Maybe you could sort of affirm that and add some color, perhaps. >> Exactly. So you're able to let's say that I have seven cars, lines of business that are asking me questions, and one of the questions they'll ask me is, we want to know, if this customer is okay to contact, and there's different avenues so you can go online, do not contact me, you can go to the bank and you can say, I don't want email, but I'll take texts. And I want no phone calls. All that information. So, seven different lines of business asked me that question in different ways. One said, "No okay to contact" the other one says, "Customer 123." All these. In each project before I got there used to be siloed. So one customer would be 100 hours for them to do that analytical work, and then another analyst would do another 100 hours on the other project. Well, now I can do that all at once. And I can do those types of searches and say, Yes, we already have that documentation. Here it is, and this is where you can find where the customer has said, "No, I don't want to get access from you by email or I've subscribed to get emails from you." >> Got it. Okay. Yeah Okay. And then I want to go back to the cloud a little bit. So you mentioned S3 Buckets. So you're moving to the Amazon cloud, at least, I'm sure you're going to get a hybrid situation there. You mentioned snowflake. What was sort of the decision to move to the cloud? Obviously, snowflake is cloud only. There's not an on-prem, version there. So what precipitated that? >> Alright, so from I've been in the data IT information field for the last 35 years. I started in the US Air Force, and have moved on from since then. And my experience with Bob Graham, was with snowflake with working with GE Capital. And that's where I met up with the team from Io-Tahoe as well. And so it's a proven so there's a couple of things one is Informatica, is worldwide known to move data. They have two products, they have the on-prem and the off-prem. I've used the on-prem and off-prem, they're both great. And it's very stable, and I'm comfortable with it. Other people are very comfortable with it. So we picked that as our batch data movement. We're moving toward probably HVR. It's not a total decision yet. But we're moving to HVR for real time data, which is changed capture data, moves it into the cloud. And then, so you're envisioning this right now. In which is you're in the S3, and you have all the data that you could possibly want. And that's JSON, all that everything is sitting in the S3 to be able to move it through into snowflake. And snowflake has proven to have a stability. You only need to learn and train your team with one thing. AWS as is completely stable at this point too. So all these avenues if you think about it, is going through from, this is your data lake, which is I would consider your S3. And even though it's not a traditional data lake like, you can touch it like a Progressive or Hadoop. And then into snowflake and then from snowflake into sandbox and so your lines of business and your data scientists just dive right in. That makes a big win. And then using Io-Tahoe with the data automation, and also their search engine. I have the ability to give the data scientists and data analysts the way of they don't need to talk to IT to get accurate information or completely accurate information from the structure. And we'll be right back. >> Yeah, so talking about snowflake and getting up to speed quickly. I know from talking to customers you can get from zero to snowflake very fast and then it sounds like the Io-Tahoe is sort of the automation cloud for your data pipeline within the cloud. Is that the right way to think about it? >> I think so. Right now I have Io-Tahoe attached to my on-prem. And I want to attach it to my off-prem eventually. So I'm using Io-Tahoe data automation right now, to bring in the data, and to start analyzing the data flows to make sure that I'm not missing anything, and that I'm not bringing over redundant data. The data warehouse that I'm working of, it's an on-prem. It's an Oracle Database, and it's 15 years old. So it has extra data in it. It has things that we don't need anymore, and Io-Tahoe's helping me shake out that extra data that does not need to be moved into my S3. So it's saving me money, when I'm moving from off-prem to on-prem. >> And so that was a challenge prior, because you couldn't get the lines of business to agree what to delete, or what was the issue there? >> Oh, it was more than that. Each line of business had their own structure within the warehouse. And then they were copying data between each other, and duplicating the data and using that. So there could be possibly three tables that have the same data in it, but it's used for different lines of business. We have identified using Io-Tahoe identified over seven terabytes in the last two months on data that has just been repetitive. It's the same exact data just sitting in a different schema. And that's not easy to find, if you only understand one schema, that's reporting for that line of business. >> More bad news for the storage companies out there. (both laughs) So far. >> It's cheap. That's what we were telling people. >> And it's true, but you still would rather not waste it, you'd like to apply it to drive more revenue. And so, I guess, let's close on where you see this thing going. Again, I know you're sort of partway through the journey, maybe you could sort of describe, where you see the phase is going and really what you want to get out of this thing, down the road, mid-term, longer term, what's your vision or your data driven organization. >> I want for the bankers to be able to walk around with an iPad in their hand, and be able to access data for that customer, really fast and be able to give them the best deal that they can get. I want Webster to be right there on top with being able to add new customers, and to be able to serve our existing customers who had bank accounts since they were 12 years old there and now our multi whatever. I want them to be able to have the best experience with our bankers. >> That's awesome. That's really what I want as a banking customer. I want my bank to know who I am, anticipate my needs, and create a great experience for me. And then let me go on with my life. And so that follow. Great story. Love your experience, your background and your knowledge. I can't thank you enough for coming on theCube. >> Now, thank you very much. And you guys have a great day. >> All right, take care. And thank you for watching everybody. Keep right there. We'll take a short break and be right back. (gentle music)

>> Narrator: From around the Globe, it's theCube with digital coverage of Enterprise Data Automation, and event series brought to you by Io-Tahoe. >> Everybody, we're back. And this is Dave Vellante, and we're covering the whole notion of Automated Data in the Enterprise. And I'm really excited to have Paula D'Amico here. Senior Vice President of Enterprise Data Architecture at Webster Bank. Paula, good to see you. Thanks for coming on. >> Hi, nice to see you, too. >> Let's start with Webster bank. You guys are kind of a regional I think New York, New England, believe it's headquartered out of Connecticut. But tell us a little bit about the bank. >> Webster bank is regional Boston, Connecticut, and New York. Very focused on in Westchester and Fairfield County. They are a really highly rated regional bank for this area. They hold quite a few awards for the area for being supportive for the community, and are really moving forward technology wise, they really want to be a data driven bank, and they want to move into a more robust group. >> We got a lot to talk about. So data driven is an interesting topic and your role as Data Architecture, is really Senior Vice President Data Architecture. So you got a big responsibility as it relates to kind of transitioning to this digital data driven bank but tell us a little bit about your role in your Organization. >> Currently, today, we have a small group that is just working toward moving into a more futuristic, more data driven data warehousing. That's our first item. And then the other item is to drive new revenue by anticipating what customers do, when they go to the bank or when they log in to their account, to be able to give them the best offer. And the only way to do that is you have timely, accurate, complete data on the customer and what's really a great value on offer something to offer that, or a new product, or to help them continue to grow their savings, or do and grow their investments. >> Okay, and I really want to get into that. But before we do, and I know you're, sort of partway through your journey, you got a lot to do. But I want to ask you about Covid, how you guys handling that? You had the government coming down and small business loans and PPP, and huge volume of business and sort of data was at the heart of that. How did you manage through that? >> We were extremely successful, because we have a big, dedicated team that understands where their data is and was able to switch much faster than a larger bank, to be able to offer the PPP Long's out to our customers within lightning speed. And part of that was is we adapted to Salesforce very for we've had Salesforce in house for over 15 years. Pretty much that was the driving vehicle to get our PPP loans in, and then developing logic quickly, but it was a 24 seven development role and get the data moving on helping our customers fill out the forms. And a lot of that was manual, but it was a large community effort. >> Think about that too. The volume was probably much higher than the volume of loans to small businesses that you're used to granting and then also the initial guidelines were very opaque. You really didn't know what the rules were, but you were expected to enforce them. And then finally, you got more clarity. So you had to essentially code that logic into the system in real time. >> I wasn't directly involved, but part of my data movement team was, and we had to change the logic overnight. So it was on a Friday night it was released, we pushed our first set of loans through, and then the logic changed from coming from the government, it changed and we had to redevelop our data movement pieces again, and we design them and send them back through. So it was definitely kind of scary, but we were completely successful. We hit a very high peak. Again, I don't know the exact number but it was in the thousands of loans, from little loans to very large loans and not one customer who applied did not get what they needed for, that was the right process and filled out the right amount. >> Well, that is an amazing story and really great support for the region, your Connecticut, the Boston area. So that's fantastic. I want to get into the rest of your story now. Let's start with some of the business drivers in banking. I mean, obviously online. A lot of people have sort of joked that many of the older people, who kind of shunned online banking would love to go into the branch and see their friendly teller had no choice, during this pandemic, to go to online. So that's obviously a big trend you mentioned, the data driven data warehouse, I want to understand that, but what at the top level, what are some of the key business drivers that are catalyzing your desire for change? >> The ability to give a customer, what they need at the time when they need it. And what I mean by that is that we have customer interactions in multiple ways. And I want to be able for the customer to walk into a bank or online and see the same format, and being able to have the same feel the same love, and also to be able to offer them the next best offer for them. But they're if they want looking for a new mortgage or looking to refinance, or whatever it is that they have that data, we have the data and that they feel comfortable using it. And that's an untethered banker. Attitude is, whatever my banker is holding and whatever the person is holding in their phone, that is the same and it's comfortable. So they don't feel that they've walked into the bank and they have to do fill out different paperwork compared to filling out paperwork on just doing it on their phone. >> You actually do want the experience to be better. And it is in many cases. Now you weren't able to do this with your existing I guess mainframe based Enterprise Data Warehouses. Is that right? Maybe talk about that a little bit? >> Yeah, we were definitely able to do it with what we have today the technology we're using. But one of the issues is that it's not timely. And you need a timely process to be able to get the customers to understand what's happening. You need a timely process so we can enhance our risk management. We can apply for fraud issues and things like that. >> Yeah, so you're trying to get more real time. The traditional EDW. It's sort of a science project. There's a few experts that know how to get it. You can so line up, the demand is tremendous. And then oftentimes by the time you get the answer, it's outdated. So you're trying to address that problem. So part of it is really the cycle time the end to end cycle time that you're progressing. And then there's, if I understand it residual benefits that are pretty substantial from a revenue opportunity, other offers that you can make to the right customer, that you maybe know, through your data, is that right? >> Exactly. It's drive new customers to new opportunities. It's enhanced the risk, and it's to optimize the banking process, and then obviously, to create new business. And the only way we're going to be able to do that is if we have the ability to look at the data right when the customer walks in the door or right when they open up their app. And by doing creating more to New York times near real time data, or the data warehouse team that's giving the lines of business the ability to work on the next best offer for that customer as well. >> But Paula, we're inundated with data sources these days. Are there other data sources that maybe had access to before, but perhaps the backlog of ingesting and cleaning in cataloging and analyzing maybe the backlog was so great that you couldn't perhaps tap some of those data sources. Do you see the potential to increase the data sources and hence the quality of the data or is that sort of premature? >> Oh, no. Exactly. Right. So right now, we ingest a lot of flat files and from our mainframe type of front end system, that we've had for quite a few years. But now that we're moving to the cloud and off-prem and on-prem, moving off-prem, into like an S3 Bucket, where that data we can process that data and get that data faster by using real time tools to move that data into a place where, like snowflake could utilize that data, or we can give it out to our market. Right now we're about we do work in batch mode still. So we're doing 24 hours. >> Okay. So when I think about the data pipeline, and the people involved, maybe you could talk a little bit about the organization. You've got, I don't know, if you have data scientists or statisticians, I'm sure you do. You got data architects, data engineers, quality engineers, developers, etc. And oftentimes, practitioners like yourself, will stress about, hey, the data is in silos. The data quality is not where we want it to be. We have to manually categorize the data. These are all sort of common data pipeline problems, if you will. Sometimes we use the term data Ops, which is sort of a play on DevOps applied to the data pipeline. Can you just sort of describe your situation in that context? >> Yeah, so we have a very large data ops team. And everyone that who is working on the data part of Webster's Bank, has been there 13 to 14 years. So they get the data, they understand it, they understand the lines of business. So it's right now. We could the we have data quality issues, just like everybody else does. But we have places in them where that gets cleansed. And we're moving toward and there was very much siloed data. The data scientists are out in the lines of business right now, which is great, because I think that's where data science belongs, we should give them and that's what we're working towards now is giving them more self service, giving them the ability to access the data in a more robust way. And it's a single source of truth. So they're not pulling the data down into their own, like Tableau dashboards, and then pushing the data back out. So they're going to more not, I don't want to say, a central repository, but a more of a robust repository, that's controlled across multiple avenues, where multiple lines of business can access that data. Is that help? >> Got it, Yes. And I think that one of the key things that I'm taking away from your last comment, is the cultural aspects of this by having the data scientists in the line of business, the lines of business will feel ownership of that data as opposed to pointing fingers criticizing the data quality. They really own that that problem, as opposed to saying, well, it's Paula's problem. >> Well, I have my problem is I have data engineers, data architects, database administrators, traditional data reporting people. And because some customers that I have that are business customers lines of business, they want to just subscribe to a report, they don't want to go out and do any data science work. And we still have to provide that. So we still want to provide them some kind of regiment that they wake up in the morning, and they open up their email, and there's the report that they subscribe to, which is great, and it works out really well. And one of the things is why we purchased Io-Tahoe was, I would have the ability to give the lines of business, the ability to do search within the data. And we'll read the data flows and data redundancy and things like that, and help me clean up the data. And also, to give it to the data analysts who say, all right, they just asked me they want this certain report. And it used to take okay, four weeks we're going to go and we're going to look at the data and then we'll come back and tell you what we can do. But now with Io-Tahoe, they're able to look at the data, and then in one or two days, they'll be able to go back and say, Yes, we have the data, this is where it is. This is where we found it. This is the data flows that we found also, which is what I call it, is the break of a column. It's where the column was created, and where it went to live as a teenager. (laughs) And then it went to die, where we archive it. And, yeah, it's this cycle of life for a column. And Io-Tahoe helps us do that. And we do data lineage is done all the time. And it's just takes a very long time and that's why we're using something that has AI in it and machine running. It's accurate, it does it the same way over and over again. If an analyst leaves, you're able to utilize something like Io-Tahoe to be able to do that work for you. Is that help? >> Yeah, so got it. So a couple things there, in researching Io-Tahoe, it seems like one of the strengths of their platform is the ability to visualize data, the data structure and actually dig into it, but also see it. And that speeds things up and gives everybody additional confidence. And then the other piece is essentially infusing AI or machine intelligence into the data pipeline, is really how you're attacking automation. And you're saying it repeatable, and then that helps the data quality and you have this virtual cycle. Maybe you could sort of affirm that and add some color, perhaps. >> Exactly. So you're able to let's say that I have seven cars, lines of business that are asking me questions, and one of the questions they'll ask me is, we want to know, if this customer is okay to contact, and there's different avenues so you can go online, do not contact me, you can go to the bank and you can say, I don't want email, but I'll take texts. And I want no phone calls. All that information. So, seven different lines of business asked me that question in different ways. One said, "No okay to contact" the other one says, "Customer 123." All these. In each project before I got there used to be siloed. So one customer would be 100 hours for them to do that analytical work, and then another analyst would do another 100 hours on the other project. Well, now I can do that all at once. And I can do those types of searches and say, Yes, we already have that documentation. Here it is, and this is where you can find where the customer has said, "No, I don't want to get access from you by email or I've subscribed to get emails from you." >> Got it. Okay. Yeah Okay. And then I want to go back to the cloud a little bit. So you mentioned S3 Buckets. So you're moving to the Amazon cloud, at least, I'm sure you're going to get a hybrid situation there. You mentioned snowflake. What was sort of the decision to move to the cloud? Obviously, snowflake is cloud only. There's not an on-prem, version there. So what precipitated that? >> Alright, so from I've been in the data IT information field for the last 35 years. I started in the US Air Force, and have moved on from since then. And my experience with Bob Graham, was with snowflake with working with GE Capital. And that's where I met up with the team from Io-Tahoe as well. And so it's a proven so there's a couple of things one is Informatica, is worldwide known to move data. They have two products, they have the on-prem and the off-prem. I've used the on-prem and off-prem, they're both great. And it's very stable, and I'm comfortable with it. Other people are very comfortable with it. So we picked that as our batch data movement. We're moving toward probably HVR. It's not a total decision yet. But we're moving to HVR for real time data, which is changed capture data, moves it into the cloud. And then, so you're envisioning this right now. In which is you're in the S3, and you have all the data that you could possibly want. And that's JSON, all that everything is sitting in the S3 to be able to move it through into snowflake. And snowflake has proven to have a stability. You only need to learn and train your team with one thing. AWS as is completely stable at this point too. So all these avenues if you think about it, is going through from, this is your data lake, which is I would consider your S3. And even though it's not a traditional data lake like, you can touch it like a Progressive or Hadoop. And then into snowflake and then from snowflake into sandbox and so your lines of business and your data scientists just dive right in. That makes a big win. And then using Io-Tahoe with the data automation, and also their search engine. I have the ability to give the data scientists and data analysts the way of they don't need to talk to IT to get accurate information or completely accurate information from the structure. And we'll be right back. >> Yeah, so talking about snowflake and getting up to speed quickly. I know from talking to customers you can get from zero to snowflake very fast and then it sounds like the Io-Tahoe is sort of the automation cloud for your data pipeline within the cloud. Is that the right way to think about it? >> I think so. Right now I have Io-Tahoe attached to my on-prem. And I want to attach it to my off-prem eventually. So I'm using Io-Tahoe data automation right now, to bring in the data, and to start analyzing the data flows to make sure that I'm not missing anything, and that I'm not bringing over redundant data. The data warehouse that I'm working of, it's an on-prem. It's an Oracle Database, and it's 15 years old. So it has extra data in it. It has things that we don't need anymore, and Io-Tahoe's helping me shake out that extra data that does not need to be moved into my S3. So it's saving me money, when I'm moving from off-prem to on-prem. >> And so that was a challenge prior, because you couldn't get the lines of business to agree what to delete, or what was the issue there? >> Oh, it was more than that. Each line of business had their own structure within the warehouse. And then they were copying data between each other, and duplicating the data and using that. So there could be possibly three tables that have the same data in it, but it's used for different lines of business. We have identified using Io-Tahoe identified over seven terabytes in the last two months on data that has just been repetitive. It's the same exact data just sitting in a different schema. And that's not easy to find, if you only understand one schema, that's reporting for that line of business. >> More bad news for the storage companies out there. (both laughs) So far. >> It's cheap. That's what we were telling people. >> And it's true, but you still would rather not waste it, you'd like to apply it to drive more revenue. And so, I guess, let's close on where you see this thing going. Again, I know you're sort of partway through the journey, maybe you could sort of describe, where you see the phase is going and really what you want to get out of this thing, down the road, mid-term, longer term, what's your vision or your data driven organization. >> I want for the bankers to be able to walk around with an iPad in their hand, and be able to access data for that customer, really fast and be able to give them the best deal that they can get. I want Webster to be right there on top with being able to add new customers, and to be able to serve our existing customers who had bank accounts since they were 12 years old there and now our multi whatever. I want them to be able to have the best experience with our bankers. >> That's awesome. That's really what I want as a banking customer. I want my bank to know who I am, anticipate my needs, and create a great experience for me. And then let me go on with my life. And so that follow. Great story. Love your experience, your background and your knowledge. I can't thank you enough for coming on theCube. >> Now, thank you very much. And you guys have a great day. >> All right, take care. And thank you for watching everybody. Keep right there. We'll take a short break and be right back. (gentle music)