(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)