>>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