>>Bly from Boston, Massachusetts. It's the cube covering ifs world conference 2019 brought to you by ifs. >>Okay. We're back in Boston, Massachusetts ifs world day one. You walked into cube Dave Vellante with Paul Gillen boss Devoss is here. He's the director of ISF I F S labs and Bob Dico who's the vice president of AI and RPA at ifs jets. Welcome. Good to see you again. Good morning bossy. We're on last year. I'm talking about innovation ifs labs. First of all, tell us about ifs labs and what you've been up to in the last 12 months. Well, I have has Lapsis a functioning as the new technology incubator. Fire Fest writes over continuously looking at opportunities to bring innovation into, into product and help our customers take advantage of all the new things out there to yeah. To, to create better businesses. And one of the things I talked about last year is how we want to be close to our customers. And I think, uh, that's what we have been doing over the pasta pasta year. Really be close to our customers. So Bob, you got, you got the cool title, AI, RPA, all the hot cool topics. So help us understand what role you guys play as ifs. As a software developer, are you building AI? Are you building RPA? Are you integrating it? Yes, yes. Get your paint. >>I mean, our value to our customers comes from wrapping up the technology, the AI, the RPA, the IOT into product in a way that it's going to help their business. So it's going to be easy to use. They're not going to need to be a technical specialist to take advantage of it. It's going to be embedded in the product in a way they can take advantage of very easily that that's the key for us as a software developer. We don't want to offer them a platform that they can just go and do their own thing. We want to sort of control it, make it easier for them. >>So I presume it's not a coincidence that you guys are on together. So this stuff starts in the labs and then your job is to commercialize it. Right? So, so take machine intelligence for example. I mean it can be so many things to so many different people. Take us back to sort of, you know, the starting point, you know, within reason of your work on machine intelligence, what you were thinking at the time, maybe some of the experiments that you did and how it ends up in the product. Oh, very good question. Right? So I think we start at a, Oh, well first of all, I think ifs has been using a machine learning at, at various points in our products for many, many years of Trumbull in our dynamic scheduling engine. We have been using neural networks to optimize fuel serve scheduling for quite some many years. >>But I think, um, if we go back like two years, what we sold is that, uh, there, there's a real potential, um, in our products that if you will take machine learning algorithms inside of the product to actually, um, help ultimately certain decisions in there, um, that could potentially help our business quite a bit. And the role of ifs lapse back in the day as that we just started experimenting, right? So we went out to different customers. Uh, we started engaging with them to see, okay, what kind of data do we have, what kind of use cases are there? And basically based on that, we sort of developed a vision around AI and a division back in the day was based on on three important aspects, human machine interaction optimization and automation. And that kind of really lended well with our customer use case. We talked quite a bit about that or the previous world conference. >>So at that point we basically decided, okay, you know what, we need to make serious work of this, uh, experimenting as boots. But at a certain point you have to conclude that the experiments were successful, which we did. And at that point we decided to look at, okay, how can we make this into a product and how to normally go system. We started engaging with them more intensively and starting to hand over in this guys, we decided the most also a good moment to bring somebody on board that actually has even more experience and knowledge in AI and what we already had as hive as labs. But that could basically take over the Baton. And say, okay, now I am going to run with it and actually start commercializing and productizing that still in collaboration with IVIS laps. But yeah, taking that next step in the road and then then Bob came onboard. >>Christian Pedersen made the point during the keynote this morning that you have to avoid the, the appeal of technology for technology's sake. You have to have it. I start with the business use case. You are both very technology, very deep into the technology. How do you keep disciplined to avoid letting the technology lead your, your activities? >>Well, both. Yeah. So, so I think a good example is what we see this world's going fronts as well. It is staying closer to customer and, and, and accepting and realizing that there is no, um, there's no use in just creating technology for sake of technology as you say yourself. So what we did here for example, is that we showcase collaboration projects with, with customers. So, for example, we show showcase a woman chair pack, which um, as a, as a manufacturing of spouting pouches down here in Massachusetts actually, uh, and they wanted to invest in robotics to get our widows. So what we basically did is actually wind into their factory literally on the factory floor and start innovating there. So instead of just thinking about, okay, how do robotics and AI for subrogations or one of our older products work together, we set, let's experiment on the shop floor off a customer instead of inside of the ivory towers. Sometimes our competitors to them, they'll start to answer your question. >>Sure. I can pick up a little, a little feasible. Yeah. Well, so in, I think the really important thing, and again, Christian touched on it this morning is not the individual technologies themselves. It's how they work together. Um, we see a lot of the underlying technologies becoming more commoditized. That's not where companies are really starting to differentiate algorithms after a while become algorithms. There's a good way of doing things. They might evolve slightly over time, but effectively you can open source a lot of these things. You can take advantage, the value comes from that next layer up. How you take those technologies together, how you can create end to end processes. So if we take something like predictive, we would have an asset. We would have sensors on that asset that would be providing real time data, uh, to an IOT system. We can combine that with historical maintenance data stored within a classic ERP system. >>We can pull that together, use machine learning on it to make a prediction for when that machine is gonna break down. And based on that prediction, we can raise a work order and if we do that over enough assets, we can then optimize our technicians. So instead of having to wait for it to break down, we can know in advance, we can plan for people to be in the right the right place. It's that end to end process where the value is. We have to bring that together in a way that we can offer it to our customers. There's certainly, you know, a lot of talk in the press about machines replacing humans. Machine of all machines have always replaced humans. But for the first time in history, it's with cognitive functions. Now it's, people get freaked out. A little bit about that. I'm hearing a theme of, of augmentation, you know, at this event. >>But I wonder if you could share your thoughts with regard to things like AI automation, robotic process automation. How are customers, you know, adopting them? Is there sort of concern up front? I mean we've talked to a number of RPA customers that, you know, initially maybe are hesitant but then say, wow, I'm automating all those tasks that I hate and sort of lean in. But at the same time, you know, it's clear that this could have an effect on people's jobs and lives. What are your thoughts? Sure. Do you want to kick off on them? Yeah, I'll know. Yeah, absolutely. That's fine. So I think in terms of the, the automation, the low level tasks, as you say, that can free up people to focus on higher value activities. Something like RPA, those bots, they can work 24, seven, they can do it error free. >>Um, it's often doing work that people don't enjoy anyway. So that tends to actually raise morale, raise productivity, and allow you to do tasks faster. And the augmentation, I think is where it gets very interesting because you need to, you often don't want to automate all your decisions. You want people to have the final say, but you want to provide them more information, better, more pertinent ways of making that decision. And so it's very important. If you can do that, then you've got to build the trust with them. If you're going to give them an AI decision that's just out of a black box and just say, there's a 70% chance of this happening. And what I founded in my career is that people don't tend to believe that or they start questioning it and that's where you have difficulty. So this is where explainable AI comes in. >>I do to be able to state clearly why that prediction is being made, what are the key drivers going into it? Or if that's not possible, at least giving them the confidence to see, well, you're not sure about this prediction. You can play around with it. You can see I'm right, but I'm going to make you more comfortable and then hopefully you're going to understand and, and sort of move with it. And then it starts sort of finding its way more naturally into the workplace. So that's, I think the key to building up successful open sexually. What it is is it's sort of giving a human the, the, the parameters the and saying, okay, now you can make the call as to whether or not you want to place that bet or make a different decision or hold off and get more data. Is that right? >>Uh, yeah. I think a lot of it is about setting the threshold and the parameters with within which you want to operate. Often if a model is very confident, either you know, a yes or a no, you probably be quite happy to let it automate. Take that three, it's the borderline decision where it gets interesting. You probably would still want someone to look over it, but you want them to do it consistently. You want them to do it using all the information to hand and say that's what you do. You're presented to them. And to add to that, um, I think we also should not forget they said a lot of our customers, a lot of companies are, are actually struggling finding quality stuff, right? I mean aging of the workforce riots, we're, we're old. I'm retiring eventually. Right? So aging of the workforce is a potential issue. >>Funding, lack of quality. Stop. So if I go back to the chair pack example I was just talking about, um, and, and, and some of the benefits they get out of that robotics projects, um, um, is of course they're saving money right there. They're saving about one point $5 million a year on money on that project, but their most important benefits for them, it's actually the fact that I have been able to move the people from the work floor doing that into higher scope positions, effectively countering the labor shortage today. They were limited in their operations, but in fact, I had two few quality stuff. And by putting the robots in, they were able to reposition those people and that's for them the most important benefits. So I think there's always a little bit of a balance. Um, but I also think we eventually need robots. >>We need ultimation to also keep up with the work that needs to be done. Maybe you can speak to Bobby, you can speak to software robots. We've, Pete with people think of robots, they tend to think of machines, but in fact software robots are, where are the a, the real growth is right now, the greatest growth is right now. How pervasive will software robots be in the workplace do you think in the three to five years? >> I think the software robots as they are now within the RPA space, um, they fulfill a sort of part of the Avril automation picture, but they're never going to be the whole thing. I see them very much as bringing different systems together, moving data between systems, allowing them to interact more effectively. But, um, within systems themselves, uh, you know, the bots can only really scratched the surface. >>They're interacting with software in the same way a human would on the whole by clicking buttons going through, et cetera, beneath the surface. Uh, you know, for example, within the ifs products we have got data understanding how people interact with our products. We can use machine learning on that data to learn, to make recommendations to do things that our software but wouldn't be able to see. So I think it's a combination. There's software bots, they're kind of on the outside looking in, but they're very good at bringing things together. And then insight you've got that sort of deeper automation to take real advantage of the individual pieces of software. >> This may be a little out there, but you guys >>are, you guys are deep into, into the next generation lot to talk right now about quantum and how we could see workable quantum computers within the next two to two to three years. How, what do you think the, the outlook is there? How is that going to shake things up? So >>let me answer this. We were actually a having an active project and I for slabs currently could looking at quantum computing, right? Um, there's a lot of promise in it. Uh, there's also a lot of unfilled, unfulfilled problems in that, right? But if you look at the, the potential, I think where it really starts playing, um, into, uh, into benefits is if the larger the, the, the optimization problems, the larger the algorithms are that we have to run, the more benefits it actually starts bringing us. So if you're asking me for an for an outlook, I say there is potential definitely, especially in optimization problems. Right. Um, but I also think that the realistic outlook is quite far out. Uh, yes, we're all experimenting it and I think it's our responsibility as ifs or ciphers laps to also look on what it could potentially mean for applications as we FSI Fs. >>But my personal opinion is the odd Lucas. Yeah. So what comes five to 10 years out? What comes first? Quantum computing or fully autonomous driverless vehicles? Oh, that's a tricky question. I mean, I would say in terms of the practical commercial application, it's going to be the latter in that much so that's quite a ways off. Yeah, I think so. Of course. Question back on on RPA, what are you guys exactly doing on RPA? Are you developing your own robotic process automation software or are you integrating, doing both say within the products? We, you know, if we think of RPA as, as this means of interacting with the graphical user interface in a way that a human would within the product. Um, we, we're thinking more in terms of automating processes using the machine learning as I mentioned, to learn from experience, et cetera. Uh, in a way that will take advantage of things like our API eighth, an API APIs that are discussed on main stage today. >>RPA is very much our way of interacting with other systems, allowing other systems when trapped with ifs, allowing us to, to send messages out. So we need to make it as easy as possible for those bots to call us. Uh, you know, that can be by making our screens nice and accessible and easy to use. But I think the way that RPA is going, a lot of the major vendors are becoming orchestrators really. They're creating these, these studios where you can drag and drop different components into to do ACR, provide cognitive services and you know, elements that you could drag and drop in would be to say, ah, take data from a file and load it into ifs and put it in a purchase order. And you can just drag that in and then it doesn't really matter how it connects to YFS. It can do that via the API. And I think it probably will say it's creating the ability to talk to ifs. That's the most important thing for us. So you're making your products a RPA ready, friendly >>you, it sounds like you're using it for your own purposes, but you're not an RPA vendor per se. You know what I'm saying? Okay. Here's how you do an automation. You're gonna integrate that with other RPA leadership product. I think we would really take a more firm partner approach to it. Right? So if a customer, I mean, there's different ways of integrating systems to get our RPA as a Google on there. There's other ways as well, right? That if a customer actually, um, wants to integrate the systems together using RPA, very good choice, we make sure that our products are as ready as much for that as possible. Of course we will look at the partner ecosystem to make sure that we have sufficient and the right partners in there that a customer has as a choice in what we recommends. But basically we say where we want to be agnostic to what kind of RPA feminists sits in there that was standing there was obviously a lot of geopolitical stuff going on with tariffs and the like. >>So not withstanding that, do you feel as though things like automation, RPA, AI will swing the pendulum back to onshore manufacturing, whether it's Europe or, or U S or is the costs still so dramatically advantageous to, you know, manufacture in China? Well, that pendulum swing in your opinion as a result of automation? Um, I have a good, good question. Um, I'm not sure it's will completely swing, but it will definitely be influenced. Right. One of the examples I've seen in the RPA space ride wire a company before we would actually have an outsourcing project in India where people would just type over D uh, DDD, the purchase orders right now. Now in RPA bolts scans. I didn't, so they don't need the Indian North shore anymore. But it's always a balance between, you know, what's the benefit of what's the cost of developing technology and that's, and it's, and, and it's almost like a macro economical sort of discussion. >>One of the discussions I had with my colleagues in Sri Lanka, um, and, and maybe completely off topic example, we were talking about carwash, right? So us in the, in the Western world we have car wash where you drive your car through, right? They don't have them in Sri Lankan. All the car washes are by hands. But the difference is because labor is cheaper there that it's actually cheaper to have people washing your car while we'd also in the us for example, that's more expensive than actually having a machine doing it. Right. So it is a, it's a macro economical sort of question that is quite interesting to see how that develops over the next couple of years. All right, Jess. Well thanks very much for coming on the cube. Great discussion. Really appreciate it. Thank you very much. You're welcome. All right. I'll keep it right there, but he gave a latte. Paul Gillen moved back. Ifs world from Boston. You watch in the queue.

>> Voiceover: Live, from Atlanta, Georgia, it's theCUBE. Covering IFS World Conference 2018. Brought to you by, IFS. >> Rebecca: Welcome back to theCUBE's live coverage of IFS World Conference 2018 here in Atlanta, Georgia. I'm your host, Rebecca Knight, along with my co-host, Jeff Frick. It's been a great day here. >> Jeff: Yes. >> We've had a lot of wonderful conversations, great panels. Last one to go, you can tell the atmosphere is getting... >> They're wheeling out all the alcohol I think... >> Exactly. Exactly. >> ...for the reception this evening. >> But we have saved best for last. We have Dan Matthews, who is the CTO of IFS and Bas De Vos who is the Director of IFS Labs. So Bas and Dan, thanks so much for joining us. >> Thank you. >> You're welcome. >> So, when I talked, we've heard a lot about IFS Apps 10, and this is the big news, but what we haven't talked about too much is Arena. Can you describe to our viewers this new user experience, and what it means? >> Alright, well, IFS Arena, like you said, it's a new user experience via past applications, and that's something that's really important to us because it's important to our customers. Because what they want to do is, they want to put great tools in the hands of the people, right? And we all know when it comes to software, how great a tool is is a large part down to the user experience, so that's why we've done it. And what we've done is create something that we think is more inspired by really well-designed consumer software, but we've adapted that for these big enterprise applications like we are doing. >> It's pretty amazing in your keynote because you showed, I think five different UI's based on different devices in the prior versions, where now you're coming to kind of a standardized single (mumbles) experience across various platforms or across various devices to actually interact with the applications. That's got to be, feel good to get that down to kind of one responsive design. >> And to a degree, that's just rescinding to reality because you used to think about, you had your PC and you had a way of doing that. And then you go to your mobile app, or maybe, I mean, people are using so many different kinds devices today. So if we were to purpose build something just for your iPad, something for your phone, something for this, something for your TV, we'd be stuck forever, right? So what we did instead, is we said, "Let's build one experience that actually adapts "to all these different environments, "and get that really, really well." It's not that easy, but in the end, it's a much better way of approaching it. >> Right, and I thought the part that I liked was as when you're new to something, you don't necessarily want a high density of information in a screen or whatever, 'cause you're just not sure, you're learning, whatever, it's new. But then as you become more experienced, obviously your comfort zone goes up, you want a lot more dense information, and really, in your work platform you demoed earlier today, you have a lot of options whether you want kind of the more consumery, more picturey, less efficient way, or do you want the "I know this well, "and I want the thick content." >> And what we basically does, we flipped it upside down, 'cause if you look at Enterprise Software, and ERP, and has to management this kind of stuff, it always used to be designed for the professional, right? And then you would try to simplify it for the newbies that're coming into the business. Can we remove some things, hide some things away, configure some things? Now we've done it the other way around. So the default is it's designed for the novice person that's just coming in seeing this for the first time. And then as you learn, as you say, you can expand and grow, and they get sort of more rich in the data you're seeing. And this is really, really important right? Because people aren't staying that long in the jobs anymore. So if you think about people moving around, they know the business, but they might not know the business applications, so they basically come in, I'm a purchasing guy, come in, pick up the purchasing system directly, that's really really important. >> Needs to be intuitive? >> Yeah, make it intuitive first, and then progressively let people discover more, rather than give all the options and all the complexity and then expect them to simplify it. That's harder. >> So, Bas, I want to talk to you a little bit about the development process and how you come up with these kind of things. Can you describe how it works at IFS Labs, what approach you take? >> Yeah of course, and then perhaps Dan can add to this a little bit later as well. But because IFS Labs is just a part of the process, right? But if you look in our general development process, for us, it's very important to stay close to our customers, right? What do our customers need today? What do they need tomorrow? And we have to basically be able to deliver functionality they need for their problems right on time. And IFS Labs plays a part in that. We are basically (mumbles) for sending before that. So we approach it a little bit the other way around. So instead of looking at a customer problem and trying to find a solution for that, we basically look ahead. We look a couple of years in the future. What kind of technologies are coming up? What kind of possibilities are there, and can we find a problem for it? And that sounds strange, right? Because we're known in the business of finding problems. But it does allow us to experiment and come up with innovative solutions that might work for tomorrow. But before we actually move that into production, or hand it over to regular R&D development, well we do step back and go to our customers and say, "Hey wait a minute, this is what we are thinking Labs, "what do you think about that? "Does it work for you, does it help you?" and validate it with them. >> So it's an interesting challenge for Labs, for looking down the road, because, and Steve Jobs' famous quote, that we don't necessarily deliver just what our customers ask for. They're not asking for things that are down the road, so you got that responsibility to look down the road. On the other hand, nobody likes technology that doesn't have a problem to solve. So you got to be delicate. Because if you just build something for the sake of building something, maybe there's some ancillary value. But at the end of the day, someone's got to use it and they got to drive direct values. So how do you kind of play that balance beyond, "Yes we listen to customers, "but there's this other stuff coming "that maybe they're not too aware of"? >> Yeah that's true, totally true, I completely agree with you. And I think that is the role of IFS Labs, right? So if we look in the overall process, the fact that we have a Labs, we don't... A license to experiment with trying out stuff, validating it with our customers, we can basically... Try it out before we actually take a decision to build something that our customers are not waiting for. So exactly the problem you just sketched, I think that our interest, IFS Labs, to resolve that. >> We have seen this happening throughout history, right? So if you look at how IET started, for us, it started with a product in IFS labs, when together we want a customer learning and understanding how they should be applied to the kind of businesses and industries that we serve. And then it went into mainstream R&D development and then we have real solutions, and now we have customers, who've been live for years, using this kind of stuff. So that is exactly the process you want to have. Try it out, and when we have a grasp on how this relates to our customers, then we up the next level of investment and take it further. >> And then, similarly, we had a project in IFS Labs that, well we tried out, and after a couple of months or even longer we said, "This is not going to work "for our customers, it's actually not helping them today. "Might be a couple years from now, but today let's stop it." >> So was this how your kind of integration of AI and machine learning into the applications took place? You looked forward, this is a cool new thing we need to play, but at the same time, we're not going to name it after a smart dead guy. (group laughing) But really bake it into the applications where it makes the most sense. And that sounds like it's kind of your execution strategy. >> Yeah definitely and AIs are a very, very, very big topic, right? It's an umbrella for so many different types of applications. Dan was talking this morning about three main areas where we think AI makes most sense for our products. It's basically human-machine interaction, predictive maintenance and service, an automation. But each of those areas, they basically have their own... Own life cycle, right? So if you look at human-machine interaction, at the morning. This morning we were talking about the IFS Arena bot. We're actually in a proper development phase. So that's much further ahead in that cycle, while other AI related topics like doing mass-automation, only your (mumbles), that's earlier in the cycle and that's still in Labs. So although AI is a big umbrella topic, the different topics in there follow that same approach. >> Can you be a little more specific about the projects you're working on, or is it top secret? >> At the World Conference everybody wants to know our secrets, but luckily, at World Conference we share them. >> Jeff: This is between us four. >> Yeah nobody's listening, right? Or watching? (laughs) So yeah at this World Conference we're hosting an innovation area. And in the innovation area, we're showcasing a wide range of basically possible technologies and how you could apply them to future business. We basically took the approach of depicting an end-to-end automatous business. So basically go all the way from mining stuff, in a mine in the ground, to using that in a factory, to producing products for the customer. And we basically build all kinds of technologies in there to make that completely automatous. Might not all be possible today, but it's really there to inspire our customers to look ahead. Some examples of the things we're using, a block chain inside enterprisesque management, mixed reality with Microsoft HoloLens to do service repairs, digital twins in virtual reality, automatous vehicles. So there's a lot of interesting stuff going on there. >> That's great, those are the great buzzwords but you put them all within application, and they're just standalone. >> Dan: What it does really well, is it kind of illustrates how these technologies are used in context... >> Right. >> Dan: With all of these things. >> That's super. >> You are an IFS veteran, >> Yes. >> You came as a developer and now here you are, CTO. Tell our viewers a little bit about how the company has changed in your opinion, and also now as you are sort of making a bigger push into North America, what we can expect. >> Well, what else changed, if I go back and I've not been with this company for more than 20 years. But what I've seen is we've got a lot more professional. Of course, we're a big organization now, and the way we run things and the way the business is run is a lot more professional. If you go back to the late '90s, this was before the dot-com boom, everybody was pouring money into the IT industry, so that was not an objective. So we were doing R&D but we were also burning money. And I think after that bubble burst, we all learned to become proper business people as well. I'll tell you one that hasn't changed, though, and that really is the kind of atmosphere that is within the company, right? How close we are to our customers, and how the customers reality always comes first and how we all help each other support. That really hasn't changed despite the fact we're so much bigger and we're 20 years old and all that kind of stuff. >> So why do you think is it 'cause maintaining culture is really, really difficult and we go to a lot of shows and we often talk about if it's a founder-led, and if they're a good CEO to double benefit, to keep that culture, but when you got turned over at the top, how do you maintain the culture that you guys have built? >> I think in the beginning, I think it was a lot of that founder-led, right? It was really led by the founders and one of the founders was our CEO for many, many years. But then it kind of got ingrained a little bit, between the Scandinavia culture. That it's quite open, quite sort of friendly, helpful, lots of hierarchical. And that then sort of spread out as the business expanded into nationally. And we kept it also on the R&D side. We do a lot of R&D in Chalinka for example. Which has a surprisingly similar feeling in the culture, actually. So I think it just got so big and so strong in the company, that it just naturally, new people come in and naturally sort of carry on with that same way of being that we've had it before. >> Rebecca: They adopted and embraced it. >> Because that was the end, Dan said when he was doing his due diligence, right? The culture was a huge piece of why he came to the company. >> I think if they were the other way around, we have seen that when we brought businesses in as well, that is, right, these guys have a similar culture to us, great, fantastic business to bring into to the IFS family. >> Jeff: Sir, you were going to say? >> I was going to say, in the end also, you're attracting people to your company and the people that are staying are also the people that feel at at home, and that feel comfortable, and that feel, I'm a little bit shorter than Dan inside the company for two years now. But basically, I feel the same with the culture, right? And it fits me as a person, and therefore I think I'm inclined to stay longer at IFS than if the culture would not fit me. And as you attract people with the same mindset together. It only gets stronger. >> Right, well Dan and Bas, thank you so much. This has been really fun last panel of the day, so we appreciate it. >> Thank you. >> Good luck on your keynote on Thursday. >> Bas: Thank you very much. >> I'm Rebecca Knight for Jeff Frick. This has been IFS World Conference 2018. We will have more after this. (light techno music)

(upbeat music) >> Welcome back to theCUBE's coverage of VEEMON 2022. We're here at the Aria in Las Vegas. Dave Vellante with David Nicholson, my co-host for the week, two days at wall to wall coverage. Jason Buffington is here, JBuff, who does some amazing work for VEEAM, former Analyst from the Enterprise Strategy Group. So he's got a real appreciation for independence data, and we're going to dig into some data. You guys, I got to say, Jason, first of all, welcome back to theCUBE. It's great to see you again. >> Yeah, two and a half years, thanks for having me back. >> Yeah, that's right. (Jason laughs) Seems like a blur. >> No doubt. >> But so here's the thing as analysts, you can appreciate this, the trend is your friend, right? and everybody just inundates you with now, ransomware. It's the trend. So you get everybody's talking about the ransomware, cyber resiliency, immutability, air gaps, et cetera. Okay, great. Technology's there, it's kind of like the NFL, everybody kind of does the same thing. >> There's a lot of wonderful buzzwords in that sentence. >> Absolutely, but what you guys have done that's different is you brought in some big time thought leadership, with data and survey work which of course as an analyst we love, but you drive strategies off of this. So you got to, I'll set it up. You got a new study out that's pivoted off of February study of 3,600 organizations, and then you follow that up with a thousand organizations that actually got hit with ransomware. So tell us more about the study and the work that you've done there. >> Yeah, I got to say I have the best job ever. So I spent seven years as an analyst. And when I decided I didn't want to be an analyst anymore, I called VEEAM and said, I'd like to get in the fight and they let me in. But they let me do independent research on their behalf. So it's kind of like being an in-house counsel. I'm an in-house analyst. And for the beginning of this year, in February, we published a report called the Data Protection Trends Report. And it was over 3000 responses, right? 28 countries around the world looking at digital transformation, the effects of COVID, where are they are on BAS and DRS. But one of the new areas we wanted to look at was how pervasive is ransomware? How does that align with BCDR overall? So some of those just big thought questions that everyone's trying to solve for. And out of that, we said, "Wow, this is really worth double clicking." And so today, actually about an hour ago we published the Ransomware Trends Report and it's a thousand organizations all of which have all been survived. They all had a ransomware attack. One of the things I think I'm most proud of for VEEAM in this particular project, we use an independent research firm. So no one knows it's VEEAM that's asking the questions. We don't have any access to the respondents along the way. I wish we did, right? >> Yeah, I bet >> Go sell 'em back up software. But of the thousands 200 were CISOs, 400 were security professionals which we don't normally interact with, 200 backup admins, 200 IT ops, and the idea was, "Okay, you've all been through a really bad day. Tell us from your four different views, how did that go? What did you solve for? What did you learn? What are you moving forward with?" And so, yeah, some great learnings all around helping us understand how do we deliver solutions that meet their needs? >> I mean, there's just not enough time here to cover all this data. And I think I like about it is, like you said, it's a blind survey. You used an independent third party whom I know they're really good. And you guys are really honest about it. It's like, it was funny that the analyst called today for the analyst meeting when Danny was saying if 54% and Dave Russell was like, it's 52%, actually ended up being 53%. (Jason laughs) So, whereas many companies would say 75%. So anyway, what were some of the more striking findings of that study? Let's get into it a little bit. >> So a couple of the ones that were really startling for me, on average about one in four organizations say they have not been hit. But since we know that ransomware has a gestation for around 200 days from first intrusions, so when you have that attack, 25% may be wrong. That's 25% in best case. Another 16% said they only got hit once in the last year. And that means 60%, right on the money got hit more than once per year. And so when you think about it's like that school bully Once they take your lunch money once and they want lunch money, again, they just come right back again. Did you fix this hole? Did you fix that hole? Cool, payday. And so that was really, really scary. Once they get in, on average organizations said 47% of their production data was encrypted. Think about that. So, and we tested for, hey, was it in the, maybe it's just in the ROBO. So on the edge where the tech isn't as good, or maybe it's in the cloud because it's in a broad attack surface. Whatever it is, turns out, doesn't matter. >> So this isn't just nibbling around the edges. >> No. >> This is going straight to the heart of the enterprise. >> 47% of production data, regardless of where it's stored, data center ROBO or cloud, on average was encrypted. But what I thought was really interesting was when you look at the four personas, the security professional and the backup admin. The person responsible for prevention or mediation, they saw a much higher rate of infection than the CSOs and the IT pros, which I think the meta point there is the closer you are to the problem. the worst this is. 47% is bad. it's worse than that. As you get closer to it. >> The other thing that struck me is that a large proportion of, I think it was a third of the companies that paid ransom. >> Oh yeah. >> Weren't able to recover it. Maybe got the keys and it didn't work or maybe they never got the keys. >> That's crazy too. And I think one thing that a lot of folks, you watch the movies and stuff and you think, "Oh, I'm going to pay the Bitcoin. I'm going to get this magic incantation key and all of a sudden it's like it never happened. That is not how this works. And so yeah. So the question actually was did you pay and did it work right? And so 52%, just at half of organization said, yes. I paid and I was able to recover it. A third of folks, 27%. So a third of those that paid, they paid they cut the check, they did the ransom, whatever, and they still couldn't get back. Almost even money by the way. So 24% paid, but could not get back. 19% did not pay, but recovered from backup. VEEAM's whole job for all of 2022 and 23 needs to be invert that number and help the other 81% say, "No, I didn't pay I just recovered." >> Well, in just a huge number of cases they attacked the backup Corpus. >> Yes. >> I mean, that's was... >> 94% >> 94%? >> 94% of the time, one of the first intrusions is to attempt to get rid of the backup repository. And in two thirds of all cases the back repository is impacted. And so when I describe this, I talk about it this way. The ransomware thief, they're selling a product. They're selling your survivability as a product. And how do you increase the likelihood that you will buy what they're selling? Get rid of the life preserver. Get rid of their only other option 'cause then they got nothing left. So yeah, two thirds, the backup password goes away. That's why VEEAM is so important around cloud and disk and tape, immutable at every level. How we do what we do. >> So what's the answer here. We hear things like immutability. We hear terms like air gap. We heard, which we don't hear often, is orchestrated recovery and automated recovery. I wonder if you could get, I want to come back to... So, okay. So you're differentiating with some thought leadership, that's nice. >> Yep. >> Okay, good. Thank you. The industry thanks you for that free service. But how about product and practices? How does VEEAM differentiate in that regard? >> Sure. Now full disclosure. So when you download that report, for every five or six pages of research, the marketing department is allowed to put in one paragraph. It says, this is our answer. They call the VEEAM perspective. That's their rebuttal. To five pages of research, they get one paragraph, 250 word count and you're done. And so there is actually a commercial... >> We're here to buy here in. (chuckles) >> To the back of that. It's how we pay for the research. >> Everybody sells an onset. (laughs) >> All right. So let's talk about the tech that actually matters though, because there actually are some good insights there. Certainly the first one is immutability. So if you don't have a survivable repository you have no options. And so we provide air gaping, whether you are cloud based. So your favorite hyper-scale or one of the tens of thousands of cloud service providers that offer VEEAM products. So you can have, immutability at the cloud layer. You can certainly have immutability at the object layer on-prem or disk. We're happy to use all your favorite DDoS and then tape. It is hard to get more air-gaped and take the tape out drive, stick it on a shelf or stick it in a white van and have it shipped down the street. So, and the fact that we aren't dependent on any architecture, means choose your favorite cloud, choose your favorite disc, choose your favorite tape and we'll make all of 'em usable and defendable. So that's super key Number one. Super key number two there's three. >> So Platform agnostic essentially. >> Yeah. >> Cloud platform agenda, >> Any cloud, any physical, we work happily with everybody. Just here for your data. So, now you know you have at least a repository, which is not affectable. The next thing is you need to know, do you actually have recoverable data? And that's two different questions. >> How do you know? Right, I mean... >> You don't. So one of my colleagues, Chris Hoff, talks about how you can have this Nalgene bottle that makes sure that no water spills. Do you know that that's water? Is it vodka? Is it poison? You don't know. You just know that nothing's spilling out of it. That's an immutable repository. Then you got to know, can you actually restore the data? And so automating test restores every night, not just did the backup log work. Only 16% actually test their backups. That breaks my heart. That means 84% got it wrong. >> And that's because it just don't have the resource or sometimes testing is dangerous. >> It can be dangerous. It can also just be hard. I mean, how do you spend something up without breaking what's already live. So several years ago, VEEAM created the sandbox is what we call a data lab. And so we create a whole framework for you with a proxy that goes in you can stand up whatever you want. You can, if file exists, you can ping it, you can ODBC SQL, you can map the exchange. I mean, you can, did it actually come up. >> You can actually run water through the recovery pipes. >> Yes. >> And tweak it so that it actually works. >> Exactly. So that's the second thing. And only 16% of organizations do. >> Wow. >> And then the third thing is orchestration. So there's a lot of complexity that happens when you recover one workload. There is a stupid amount of complexity happens when you try cover a whole site or old system, or I don't know, 47% of your infrastructure. And so what can you do to orchestrate that to remediate that time? Those are the three things we found. >> So, and that orchestration piece, a number of customers that were in the survey were trying to recover manually. Which is a formula for failure. A number of, I think the largest percentage were scripts which I want you to explain why scripts are problematic. And then there was a portion that was actually doing it right. Maybe it was bigger, maybe it was a quarter that was doing orchestrated recovery. But talk about why scripts are not the right approach. >> So there were two numbers in there. So there was 16% test the ability to recover, 25% use orchestration as part of the recovery process. And so the problem where it is, is that okay, if I'm doing it manually, think about, okay, I've stood back up these databases. Now I have to reconnect the apps. Now I have to re IP. I mean, there's lots of stuff to stand up any given application. Scripts says, "Hey, I'm going to write those steps down." But we all know that, that IT and infrastructure is a living breathing thing. And so those scripts are good for about the day after you put the application in, and after that they start to gather dust pretty quick. The thing about orchestration is, if you only have a script, it's as frequently as you run the script that's all you know. But if you do a workflow, have it run the workflow every night, every week, every month. Test it the same way. That's why that's such a key to success. And for us that's VEEAM disaster recovery orchestra tour. That's a product that orchestrates all the stuff that VEEAM users know and love about our backend recovery engine. >> So imagine you're, you are an Excel user, you're using macros. And I got to go in here, click on that, doing this, sort of watching you and it repeats that, but then something changes. New data or new compliance issue, whatever... >> That got renamed directly. >> So you're going to have to go in and manually change that. How do you, what's the technology behind automated orchestration? What's the magic there? >> The magic is a product that we call orchestrator. And so it actually takes all of those steps and you actually define each step along the way. You define the IP addresses. You define the paths. You define where it's going to go. And then it runs the job in test mode every night, every week, whatever. And so if there's a problem with any step along the way, it gives you the report. Fix those things before you need it. That's the power of orchestrator. >> So what are you guys doing with this study? What can we expect? >> So the report came out today. In a couple weeks, we'll release regional versions of the same data. The reason that we survey at scale is because we want to know what's different in a PJ versus the Americas versus Europe and all those different personas. So we'll be releasing regional versions of the data along the way. And then we'll enable road shows and events and all the other stuff that happens and our partners get it so they can use it for consulting, et cetera. >> So you saw differences in persona. In terms of their perception, the closer you were to the problem, the more obvious it was, did you have enough end to discern its pearly? I know that's why you're due the drill downs but did you sense any preliminary data you can share on regions as West getting hit harder or? >> So attack rate's actually pretty consistent. Especially because so many criminals now use ransomware as a service. I mean, you're standing it up and you're spreading wide and you're seeing what hits. Where we actually saw pretty distinct geographic problems is the cloud is not of as available in all segments. Expertise around preventative measures and remediation is not available in all segments, in all regions. And so really geographic split and segment split and the lack of expertise in some of the more advanced technologies you want to use, that's really where things break down. Common attack plane, uncommon disadvantage in recovery. >> Great stuff. I want to dig in more. I probably have a few more questions if you don't mind, I can email you or give you a call. It's Jason Buffington. Thanks so much for coming on theCUBE. >> Thanks for having me. >> All right, keep it right there. You're watching theCUBE's live coverage of VEEAMON 2022. We're here in person in Las Vegas, huge hybrid audience. Keep it right there, be right back. (upbeat music)