(soothing music) >> Hey everyone, welcome to this event, "Build Your Cloud Center of Excellence." I'm your host, Lisa Martin. In the next 15 minutes or so my guest and I are going to be talking about redefining cloud operations, an application modernization for customers, and specifically how partners are helping to speed up that process. As you saw on our first two segments, we talked about problems enterprises are facing with cloud operations. We talked about redefining cloud operations as well to solve these problems. This segment is going to be focusing on how Hitachi Vantara's partners are really helping to speed up that process. We've got Johnson Controls here to talk about their partnership with Hitachi Vantara. Please welcome both of my guests, Prem Balasubramanian is with us, SVP and CTO Digital Solutions at Hitachi Vantara. And Suresh Mothikuru, SVP Customer Success Platform Engineering and Reliability Engineering from Johnson Controls. Gentlemen, welcome to the program, great to have you. >> Thank. >> Thank you, Lisa. >> First question is to both of you and Suresh, we'll start with you. We want to understand, you know, the cloud operations landscape is increasingly complex. We've talked a lot about that in this program. Talk to us, Suresh, about some of the biggest challenges and pin points that you faced with respect to that. >> Thank you. I think it's a great question. I mean, cloud has evolved a lot in the last 10 years. You know, when we were talking about a single cloud whether it's Azure or AWS and GCP, and that was complex enough. Now we are talking about multi-cloud and hybrid and you look at Johnson Controls, we have Azure we have AWS, we have GCP, we have Alibaba and we also support on-prem. So the architecture has become very, very complex and the complexity has grown so much that we are now thinking about whether we should be cloud native or cloud agnostic. So I think, I mean, sometimes it's hard to even explain the complexity because people think, oh, "When you go to cloud, everything is simplified." Cloud does give you a lot of simplicity, but it also really brings a lot more complexity along with it. So, and then next one is pretty important is, you know, generally when you look at cloud services, you have plenty of services that are offered within a cloud, 100, 150 services, 200 services. Even within those companies, you take AWS they might not know, an individual resource might not know about all the services we see. That's a big challenge for us as a customer to really understand each of the service that is provided in these, you know, clouds, well, doesn't matter which one that is. And the third one is pretty big, at least at the CTO the CIO, and the senior leadership level, is cost. Cost is a major factor because cloud, you know, will eat you up if you cannot manage it. If you don't have a good cloud governance process it because every minute you are in it, it's burning cash. So I think if you ask me, these are the three major things that I am facing day to day and that's where I use my partners, which I'll touch base down the line. >> Perfect, we'll talk about that. So Prem, I imagine that these problems are not unique to Johnson Controls or JCI, as you may hear us refer to it. Talk to me Prem about some of the other challenges that you're seeing within the customer landscape. >> So, yeah, I agree, Lisa, these are not very specific to JCI, but there are specific issues in JCI, right? So the way we think about these are, there is a common issue when people go to the cloud and there are very specific and unique issues for businesses, right? So JCI, and we will talk about this in the episode as we move forward. I think Suresh and his team have done some phenomenal step around how to manage this complexity. But there are customers who have a lesser complex cloud which is, they don't go to Alibaba, they don't have footprint in all three clouds. So their multi-cloud footprint could be a bit more manageable, but still struggle with a lot of the same problems around cost, around security, around talent. Talent is a big thing, right? And in Suresh's case I think it's slightly more exasperated because every cloud provider Be it AWS, JCP, or Azure brings in hundreds of services and there is nobody, including many of us, right? We learn every day, nowadays, right? It's not that there is one service integrator who knows all, while technically people can claim as a part of sales. But in reality all of us are continuing to learn in this landscape. And if you put all of this equation together with multiple clouds the complexity just starts to exponentially grow. And that's exactly what I think JCI is experiencing and Suresh's team has been experiencing, and we've been working together. But the common problems are around security talent and cost management of this, right? Those are my three things. And one last thing that I would love to say before we move away from this question is, if you think about cloud operations as a concept that's evolving over the last few years, and I have touched upon this in the previous episode as well, Lisa, right? If you take architectures, we've gone into microservices, we've gone into all these server-less architectures all the fancy things that we want. That helps us go to market faster, be more competent to as a business. But that's not simplified stuff, right? That's complicated stuff. It's a lot more distributed. Second, again, we've advanced and created more modern infrastructure because all of what we are talking is platform as a service, services on the cloud that we are consuming, right? In the same case with development we've moved into a DevOps model. We kind of click a button put some code in a repository, the code starts to run in production within a minute, everything else is automated. But then when we get to operations we are still stuck in a very old way of looking at cloud as an infrastructure, right? So you've got an infra team, you've got an app team, you've got an incident management team, you've got a soft knock, everything. But again, so Suresh can talk about this more because they are making significant strides in thinking about this as a single workload, and how do I apply engineering to go manage this? Because a lot of it is codified, right? So automation. Anyway, so that's kind of where the complexity is and how we are thinking, including JCI as a partner thinking about taming that complexity as we move forward. >> Suresh, let's talk about that taming the complexity. You guys have both done a great job of articulating the ostensible challenges that are there with cloud, especially multi-cloud environments that you're living in. But Suresh, talk about the partnership with Hitachi Vantara. How is it helping to dial down some of those inherent complexities? >> I mean, I always, you know, I think I've said this to Prem multiple times. I treat my partners as my internal, you know, employees. I look at Prem as my coworker or my peers. So the reason for that is I want Prem to have the same vested interest as a partner in my success or JCI success and vice versa, isn't it? I think that's how we operate and that's how we have been operating. And I think I would like to thank Prem and Hitachi Vantara for that really been an amazing partnership. And as he was saying, we have taken a completely holistic approach to how we want to really be in the market and play in the market to our customers. So if you look at my jacket it talks about OpenBlue platform. This is what JCI is building, that we are building this OpenBlue digital platform. And within that, my team, along with Prem's or Hitachi's, we have built what we call as Polaris. It's a technical platform where our apps can run. And this platform is automated end-to-end from a platform engineering standpoint. We stood up a platform engineering organization, a reliability engineering organization, as well as a support organization where Hitachi played a role. As I said previously, you know, for me to scale I'm not going to really have the talent and the knowledge of every function that I'm looking at. And Hitachi, not only they brought the talent but they also brought what he was talking about, Harc. You know, they have set up a lot and now we can leverage it. And they also came up with some really interesting concepts. I went and met them in India. They came up with this concept called IPL. Okay, what is that? They really challenged all their employees that's working for GCI to come up with innovative ideas to solve problems proactively, which is self-healing. You know, how you do that? So I think partners, you know, if they become really vested in your interests, they can do wonders for you. And I think in this case Hitachi is really working very well for us and in many aspects. And I'm leveraging them... You started with support, now I'm leveraging them in the automation, the platform engineering, as well as in the reliability engineering and then in even in the engineering spaces. And that like, they are my end-to-end partner right now? >> So you're really taking that holistic approach that you talked about and it sounds like it's a very collaborative two-way street partnership. Prem, I want to go back to, Suresh mentioned Harc. Talk a little bit about what Harc is and then how partners fit into Hitachi's Harc strategy. >> Great, so let me spend like a few seconds on what Harc is. Lisa, again, I know we've been using the term. Harc stands for Hitachi application reliability sectors. Now the reason we thought about Harc was, like I said in the beginning of this segment, there is an illusion from an architecture standpoint to be more modern, microservices, server-less, reactive architecture, so on and so forth. There is an illusion in your development methodology from Waterfall to agile, to DevOps to lean, agile to path program, whatever, right? Extreme program, so on and so forth. There is an evolution in the space of infrastructure from a point where you were buying these huge humongous servers and putting it in your data center to a point where people don't even see servers anymore, right? You buy it, by a click of a button you don't know the size of it. All you know is a, it's (indistinct) whatever that name means. Let's go provision it on the fly, get go, get your work done, right? When all of this is advanced when you think about operations people have been solving the problem the way they've been solving it 20 years back, right? That's the issue. And Harc was conceived exactly to fix that particular problem, to think about a modern way of operating a modern workload, right? That's exactly what Harc. So it brings together finest engineering talent. So the teams are trained in specific ways of working. We've invested and implemented some of the IP, we work with the best of the breed partner ecosystem, and I'll talk about that in a minute. And we've got these facilities in Dallas and I am talking from my office in Dallas, which is a Harc facility in the US from where we deliver for our customers. And then back in Hyderabad, we've got one more that we opened and these are facilities from where we deliver Harc services for our customers as well, right? And then we are expanding it in Japan and Portugal as we move into 23. That's kind of the plan that we are thinking through. However, that's what Harc is, Lisa, right? That's our solution to this cloud complexity problem. Right? >> Got it, and it sounds like it's going quite global, which is fantastic. So Suresh, I want to have you expand a bit on the partnership, the partner ecosystem and the role that it plays. You talked about it a little bit but what role does the partner ecosystem play in really helping JCI to dial down some of those challenges and the inherent complexities that we talked about? >> Yeah, sure. I think partners play a major role and JCI is very, very good at it. I mean, I've joined JCI 18 months ago, JCI leverages partners pretty extensively. As I said, I leverage Hitachi for my, you know, A group and the (indistinct) space and the cloud operations space, and they're my primary partner. But at the same time, we leverage many other partners. Well, you know, Accenture, SCL, and even on the tooling side we use Datadog and (indistinct). All these guys are major partners of our because the way we like to pick partners is based on our vision and where we want to go. And pick the right partner who's going to really, you know make you successful by investing their resources in you. And what I mean by that is when you have a partner, partner knows exactly what kind of skillset is needed for this customer, for them to really be successful. As I said earlier, we cannot really get all the skillset that we need, we rely on the partners and partners bring the the right skillset, they can scale. I can tell Prem tomorrow, "Hey, I need two parts by next week", and I guarantee it he's going to bring two parts to me. So they let you scale, they let you move fast. And I'm a big believer, in today's day and age, to get things done fast and be more agile. I'm not worried about failure, but for me moving fast is very, very important. And partners really do a very good job bringing that. But I think then they also really make you think, isn't it? Because one thing I like about partners they make you innovate whether they know it or not but they do because, you know, they will come and ask you questions about, "Hey, tell me why you are doing this. Can I review your architecture?" You know, and then they will try to really say I don't think this is going to work. Because they work with so many different clients, not JCI, they bring all that expertise and that's what I look from them, you know, just not, you know, do a T&M job for me. I ask you to do this go... They just bring more than that. That's how I pick my partners. And that's how, you know, Hitachi's Vantara is definitely one of a good partner from that sense because they bring a lot more innovation to the table and I appreciate about that. >> It sounds like, it sounds like a flywheel of innovation. >> Yeah. >> I love that. Last question for both of you, which we're almost out of time here, Prem, I want to go back to you. So I'm a partner, I'm planning on redefining CloudOps at my company. What are the two things you want me to remember from Hitachi Vantara's perspective? >> So before I get to that question, Lisa, the partners that we work with are slightly different from from the partners that, again, there are some similar partners. There are some different partners, right? For example, we pick and choose especially in the Harc space, we pick and choose partners that are more future focused, right? We don't care if they are huge companies or small companies. We go after companies that are future focused that are really, really nimble and can change for our customers need because it's not our need, right? When I pick partners for Harc my ultimate endeavor is to ensure, in this case because we've got (indistinct) GCI on, we are able to operate (indistinct) with the level of satisfaction above and beyond that they're expecting from us. And whatever I don't have I need to get from my partners so that I bring this solution to Suresh. As opposed to bringing a whole lot of people and making them stand in front of Suresh. So that's how I think about partners. What do I want them to do from, and we've always done this so we do workshops with our partners. We just don't go by tools. When we say we are partnering with X, Y, Z, we do workshops with them and we say, this is how we are thinking. Either you build it in your roadmap that helps us leverage you, continue to leverage you. And we do have minimal investments where we fix gaps. We're building some utilities for us to deliver the best service to our customers. And our intention is not to build a product to compete with our partner. Our intention is to just fill the wide space until they go build it into their product suite that we can then leverage it for our customers. So always think about end customers and how can we make it easy for them? Because for all the tool vendors out there seeing this and wanting to partner with Hitachi the biggest thing is tools sprawl, especially on the cloud is very real. For every problem on the cloud. I have a billion tools that are being thrown at me as Suresh if I'm putting my installation and it's not easy at all. It's so confusing. >> Yeah. >> So that's what we want. We want people to simplify that landscape for our end customers, and we are looking at partners that are thinking through the simplification not just making money. >> That makes perfect sense. There really is a very strong symbiosis it sounds like, in the partner ecosystem. And there's a lot of enablement that goes on back and forth it sounds like as well, which is really, to your point it's all about the end customers and what they're expecting. Suresh, last question for you is which is the same one, if I'm a partner what are the things that you want me to consider as I'm planning to redefine CloudOps at my company? >> I'll keep it simple. In my view, I mean, we've touched upon it in multiple facets in this interview about that, the three things. First and foremost, reliability. You know, in today's day and age my products has to be reliable, available and, you know, make sure that the customer's happy with what they're really dealing with, number one. Number two, my product has to be secure. Security is super, super important, okay? And number three, I need to really make sure my customers are getting the value so I keep my cost low. So these three is what I would focus and what I expect from my partners. >> Great advice, guys. Thank you so much for talking through this with me and really showing the audience how strong the partnership is between Hitachi Vantara and JCI. What you're doing together, we'll have to talk to you again to see where things go but we really appreciate your insights and your perspectives. Thank you. >> Thank you, Lisa. >> Thanks Lisa, thanks for having us. >> My pleasure. For my guests, I'm Lisa Martin. Thank you so much for watching. (soothing music)

(soothing music) >> Hey everyone, welcome to this event, "Build Your Cloud Center of Excellence." I'm your host, Lisa Martin. In the next 15 minutes or so my guest and I are going to be talking about redefining cloud operations, an application modernization for customers, and specifically how partners are helping to speed up that process. As you saw on our first two segments, we talked about problems enterprises are facing with cloud operations. We talked about redefining cloud operations as well to solve these problems. This segment is going to be focusing on how Hitachi Vantara's partners are really helping to speed up that process. We've got Johnson Controls here to talk about their partnership with Hitachi Vantara. Please welcome both of my guests, Prem Balasubramanian is with us, SVP and CTO Digital Solutions at Hitachi Vantara. And Suresh Mothikuru, SVP Customer Success Platform Engineering and Reliability Engineering from Johnson Controls. Gentlemen, welcome to the program, great to have you. >> Thank. >> Thank you, Lisa. >> First question is to both of you and Suresh, we'll start with you. We want to understand, you know, the cloud operations landscape is increasingly complex. We've talked a lot about that in this program. Talk to us, Suresh, about some of the biggest challenges and pin points that you faced with respect to that. >> Thank you. I think it's a great question. I mean, cloud has evolved a lot in the last 10 years. You know, when we were talking about a single cloud whether it's Azure or AWS and GCP, and that was complex enough. Now we are talking about multi-cloud and hybrid and you look at Johnson Controls, we have Azure we have AWS, we have GCP, we have Alibaba and we also support on-prem. So the architecture has become very, very complex and the complexity has grown so much that we are now thinking about whether we should be cloud native or cloud agnostic. So I think, I mean, sometimes it's hard to even explain the complexity because people think, oh, "When you go to cloud, everything is simplified." Cloud does give you a lot of simplicity, but it also really brings a lot more complexity along with it. So, and then next one is pretty important is, you know, generally when you look at cloud services, you have plenty of services that are offered within a cloud, 100, 150 services, 200 services. Even within those companies, you take AWS they might not know, an individual resource might not know about all the services we see. That's a big challenge for us as a customer to really understand each of the service that is provided in these, you know, clouds, well, doesn't matter which one that is. And the third one is pretty big, at least at the CTO the CIO, and the senior leadership level, is cost. Cost is a major factor because cloud, you know, will eat you up if you cannot manage it. If you don't have a good cloud governance process it because every minute you are in it, it's burning cash. So I think if you ask me, these are the three major things that I am facing day to day and that's where I use my partners, which I'll touch base down the line. >> Perfect, we'll talk about that. So Prem, I imagine that these problems are not unique to Johnson Controls or JCI, as you may hear us refer to it. Talk to me Prem about some of the other challenges that you're seeing within the customer landscape. >> So, yeah, I agree, Lisa, these are not very specific to JCI, but there are specific issues in JCI, right? So the way we think about these are, there is a common issue when people go to the cloud and there are very specific and unique issues for businesses, right? So JCI, and we will talk about this in the episode as we move forward. I think Suresh and his team have done some phenomenal step around how to manage this complexity. But there are customers who have a lesser complex cloud which is, they don't go to Alibaba, they don't have footprint in all three clouds. So their multi-cloud footprint could be a bit more manageable, but still struggle with a lot of the same problems around cost, around security, around talent. Talent is a big thing, right? And in Suresh's case I think it's slightly more exasperated because every cloud provider Be it AWS, JCP, or Azure brings in hundreds of services and there is nobody, including many of us, right? We learn every day, nowadays, right? It's not that there is one service integrator who knows all, while technically people can claim as a part of sales. But in reality all of us are continuing to learn in this landscape. And if you put all of this equation together with multiple clouds the complexity just starts to exponentially grow. And that's exactly what I think JCI is experiencing and Suresh's team has been experiencing, and we've been working together. But the common problems are around security talent and cost management of this, right? Those are my three things. And one last thing that I would love to say before we move away from this question is, if you think about cloud operations as a concept that's evolving over the last few years, and I have touched upon this in the previous episode as well, Lisa, right? If you take architectures, we've gone into microservices, we've gone into all these server-less architectures all the fancy things that we want. That helps us go to market faster, be more competent to as a business. But that's not simplified stuff, right? That's complicated stuff. It's a lot more distributed. Second, again, we've advanced and created more modern infrastructure because all of what we are talking is platform as a service, services on the cloud that we are consuming, right? In the same case with development we've moved into a DevOps model. We kind of click a button put some code in a repository, the code starts to run in production within a minute, everything else is automated. But then when we get to operations we are still stuck in a very old way of looking at cloud as an infrastructure, right? So you've got an infra team, you've got an app team, you've got an incident management team, you've got a soft knock, everything. But again, so Suresh can talk about this more because they are making significant strides in thinking about this as a single workload, and how do I apply engineering to go manage this? Because a lot of it is codified, right? So automation. Anyway, so that's kind of where the complexity is and how we are thinking, including JCI as a partner thinking about taming that complexity as we move forward. >> Suresh, let's talk about that taming the complexity. You guys have both done a great job of articulating the ostensible challenges that are there with cloud, especially multi-cloud environments that you're living in. But Suresh, talk about the partnership with Hitachi Vantara. How is it helping to dial down some of those inherent complexities? >> I mean, I always, you know, I think I've said this to Prem multiple times. I treat my partners as my internal, you know, employees. I look at Prem as my coworker or my peers. So the reason for that is I want Prem to have the same vested interest as a partner in my success or JCI success and vice versa, isn't it? I think that's how we operate and that's how we have been operating. And I think I would like to thank Prem and Hitachi Vantara for that really been an amazing partnership. And as he was saying, we have taken a completely holistic approach to how we want to really be in the market and play in the market to our customers. So if you look at my jacket it talks about OpenBlue platform. This is what JCI is building, that we are building this OpenBlue digital platform. And within that, my team, along with Prem's or Hitachi's, we have built what we call as Polaris. It's a technical platform where our apps can run. And this platform is automated end-to-end from a platform engineering standpoint. We stood up a platform engineering organization, a reliability engineering organization, as well as a support organization where Hitachi played a role. As I said previously, you know, for me to scale I'm not going to really have the talent and the knowledge of every function that I'm looking at. And Hitachi, not only they brought the talent but they also brought what he was talking about, Harc. You know, they have set up a lot and now we can leverage it. And they also came up with some really interesting concepts. I went and met them in India. They came up with this concept called IPL. Okay, what is that? They really challenged all their employees that's working for GCI to come up with innovative ideas to solve problems proactively, which is self-healing. You know, how you do that? So I think partners, you know, if they become really vested in your interests, they can do wonders for you. And I think in this case Hitachi is really working very well for us and in many aspects. And I'm leveraging them... You started with support, now I'm leveraging them in the automation, the platform engineering, as well as in the reliability engineering and then in even in the engineering spaces. And that like, they are my end-to-end partner right now? >> So you're really taking that holistic approach that you talked about and it sounds like it's a very collaborative two-way street partnership. Prem, I want to go back to, Suresh mentioned Harc. Talk a little bit about what Harc is and then how partners fit into Hitachi's Harc strategy. >> Great, so let me spend like a few seconds on what Harc is. Lisa, again, I know we've been using the term. Harc stands for Hitachi application reliability sectors. Now the reason we thought about Harc was, like I said in the beginning of this segment, there is an illusion from an architecture standpoint to be more modern, microservices, server-less, reactive architecture, so on and so forth. There is an illusion in your development methodology from Waterfall to agile, to DevOps to lean, agile to path program, whatever, right? Extreme program, so on and so forth. There is an evolution in the space of infrastructure from a point where you were buying these huge humongous servers and putting it in your data center to a point where people don't even see servers anymore, right? You buy it, by a click of a button you don't know the size of it. All you know is a, it's (indistinct) whatever that name means. Let's go provision it on the fly, get go, get your work done, right? When all of this is advanced when you think about operations people have been solving the problem the way they've been solving it 20 years back, right? That's the issue. And Harc was conceived exactly to fix that particular problem, to think about a modern way of operating a modern workload, right? That's exactly what Harc. So it brings together finest engineering talent. So the teams are trained in specific ways of working. We've invested and implemented some of the IP, we work with the best of the breed partner ecosystem, and I'll talk about that in a minute. And we've got these facilities in Dallas and I am talking from my office in Dallas, which is a Harc facility in the US from where we deliver for our customers. And then back in Hyderabad, we've got one more that we opened and these are facilities from where we deliver Harc services for our customers as well, right? And then we are expanding it in Japan and Portugal as we move into 23. That's kind of the plan that we are thinking through. However, that's what Harc is, Lisa, right? That's our solution to this cloud complexity problem. Right? >> Got it, and it sounds like it's going quite global, which is fantastic. So Suresh, I want to have you expand a bit on the partnership, the partner ecosystem and the role that it plays. You talked about it a little bit but what role does the partner ecosystem play in really helping JCI to dial down some of those challenges and the inherent complexities that we talked about? >> Yeah, sure. I think partners play a major role and JCI is very, very good at it. I mean, I've joined JCI 18 months ago, JCI leverages partners pretty extensively. As I said, I leverage Hitachi for my, you know, A group and the (indistinct) space and the cloud operations space, and they're my primary partner. But at the same time, we leverage many other partners. Well, you know, Accenture, SCL, and even on the tooling side we use Datadog and (indistinct). All these guys are major partners of our because the way we like to pick partners is based on our vision and where we want to go. And pick the right partner who's going to really, you know make you successful by investing their resources in you. And what I mean by that is when you have a partner, partner knows exactly what kind of skillset is needed for this customer, for them to really be successful. As I said earlier, we cannot really get all the skillset that we need, we rely on the partners and partners bring the the right skillset, they can scale. I can tell Prem tomorrow, "Hey, I need two parts by next week", and I guarantee it he's going to bring two parts to me. So they let you scale, they let you move fast. And I'm a big believer, in today's day and age, to get things done fast and be more agile. I'm not worried about failure, but for me moving fast is very, very important. And partners really do a very good job bringing that. But I think then they also really make you think, isn't it? Because one thing I like about partners they make you innovate whether they know it or not but they do because, you know, they will come and ask you questions about, "Hey, tell me why you are doing this. Can I review your architecture?" You know, and then they will try to really say I don't think this is going to work. Because they work with so many different clients, not JCI, they bring all that expertise and that's what I look from them, you know, just not, you know, do a T&M job for me. I ask you to do this go... They just bring more than that. That's how I pick my partners. And that's how, you know, Hitachi's Vantara is definitely one of a good partner from that sense because they bring a lot more innovation to the table and I appreciate about that. >> It sounds like, it sounds like a flywheel of innovation. >> Yeah. >> I love that. Last question for both of you, which we're almost out of time here, Prem, I want to go back to you. So I'm a partner, I'm planning on redefining CloudOps at my company. What are the two things you want me to remember from Hitachi Vantara's perspective? >> So before I get to that question, Lisa, the partners that we work with are slightly different from from the partners that, again, there are some similar partners. There are some different partners, right? For example, we pick and choose especially in the Harc space, we pick and choose partners that are more future focused, right? We don't care if they are huge companies or small companies. We go after companies that are future focused that are really, really nimble and can change for our customers need because it's not our need, right? When I pick partners for Harc my ultimate endeavor is to ensure, in this case because we've got (indistinct) GCI on, we are able to operate (indistinct) with the level of satisfaction above and beyond that they're expecting from us. And whatever I don't have I need to get from my partners so that I bring this solution to Suresh. As opposed to bringing a whole lot of people and making them stand in front of Suresh. So that's how I think about partners. What do I want them to do from, and we've always done this so we do workshops with our partners. We just don't go by tools. When we say we are partnering with X, Y, Z, we do workshops with them and we say, this is how we are thinking. Either you build it in your roadmap that helps us leverage you, continue to leverage you. And we do have minimal investments where we fix gaps. We're building some utilities for us to deliver the best service to our customers. And our intention is not to build a product to compete with our partner. Our intention is to just fill the wide space until they go build it into their product suite that we can then leverage it for our customers. So always think about end customers and how can we make it easy for them? Because for all the tool vendors out there seeing this and wanting to partner with Hitachi the biggest thing is tools sprawl, especially on the cloud is very real. For every problem on the cloud. I have a billion tools that are being thrown at me as Suresh if I'm putting my installation and it's not easy at all. It's so confusing. >> Yeah. >> So that's what we want. We want people to simplify that landscape for our end customers, and we are looking at partners that are thinking through the simplification not just making money. >> That makes perfect sense. There really is a very strong symbiosis it sounds like, in the partner ecosystem. And there's a lot of enablement that goes on back and forth it sounds like as well, which is really, to your point it's all about the end customers and what they're expecting. Suresh, last question for you is which is the same one, if I'm a partner what are the things that you want me to consider as I'm planning to redefine CloudOps at my company? >> I'll keep it simple. In my view, I mean, we've touched upon it in multiple facets in this interview about that, the three things. First and foremost, reliability. You know, in today's day and age my products has to be reliable, available and, you know, make sure that the customer's happy with what they're really dealing with, number one. Number two, my product has to be secure. Security is super, super important, okay? And number three, I need to really make sure my customers are getting the value so I keep my cost low. So these three is what I would focus and what I expect from my partners. >> Great advice, guys. Thank you so much for talking through this with me and really showing the audience how strong the partnership is between Hitachi Vantara and JCI. What you're doing together, we'll have to talk to you again to see where things go but we really appreciate your insights and your perspectives. Thank you. >> Thank you, Lisa. >> Thanks Lisa, thanks for having us. >> My pleasure. For my guests, I'm Lisa Martin. Thank you so much for watching. (soothing music)

(soothing music) >> Hey everyone, welcome to this event, "Build Your Cloud Center of Excellence." I'm your host, Lisa Martin. In the next 15 minutes or so my guest and I are going to be talking about redefining cloud operations, an application modernization for customers, and specifically how partners are helping to speed up that process. As you saw on our first two segments, we talked about problems enterprises are facing with cloud operations. We talked about redefining cloud operations as well to solve these problems. This segment is going to be focusing on how Hitachi Vantara's partners are really helping to speed up that process. We've got Johnson Controls here to talk about their partnership with Hitachi Vantara. Please welcome both of my guests, Prem Balasubramanian is with us, SVP and CTO Digital Solutions at Hitachi Vantara. And Suresh Mothikuru, SVP Customer Success Platform Engineering and Reliability Engineering from Johnson Controls. Gentlemen, welcome to the program, great to have you. >> Thank. >> Thank you, Lisa. >> First question is to both of you and Suresh, we'll start with you. We want to understand, you know, the cloud operations landscape is increasingly complex. We've talked a lot about that in this program. Talk to us, Suresh, about some of the biggest challenges and pin points that you faced with respect to that. >> Thank you. I think it's a great question. I mean, cloud has evolved a lot in the last 10 years. You know, when we were talking about a single cloud whether it's Azure or AWS and GCP, and that was complex enough. Now we are talking about multi-cloud and hybrid and you look at Johnson Controls, we have Azure we have AWS, we have GCP, we have Alibaba and we also support on-prem. So the architecture has become very, very complex and the complexity has grown so much that we are now thinking about whether we should be cloud native or cloud agnostic. So I think, I mean, sometimes it's hard to even explain the complexity because people think, oh, "When you go to cloud, everything is simplified." Cloud does give you a lot of simplicity, but it also really brings a lot more complexity along with it. So, and then next one is pretty important is, you know, generally when you look at cloud services, you have plenty of services that are offered within a cloud, 100, 150 services, 200 services. Even within those companies, you take AWS they might not know, an individual resource might not know about all the services we see. That's a big challenge for us as a customer to really understand each of the service that is provided in these, you know, clouds, well, doesn't matter which one that is. And the third one is pretty big, at least at the CTO the CIO, and the senior leadership level, is cost. Cost is a major factor because cloud, you know, will eat you up if you cannot manage it. If you don't have a good cloud governance process it because every minute you are in it, it's burning cash. So I think if you ask me, these are the three major things that I am facing day to day and that's where I use my partners, which I'll touch base down the line. >> Perfect, we'll talk about that. So Prem, I imagine that these problems are not unique to Johnson Controls or JCI, as you may hear us refer to it. Talk to me Prem about some of the other challenges that you're seeing within the customer landscape. >> So, yeah, I agree, Lisa, these are not very specific to JCI, but there are specific issues in JCI, right? So the way we think about these are, there is a common issue when people go to the cloud and there are very specific and unique issues for businesses, right? So JCI, and we will talk about this in the episode as we move forward. I think Suresh and his team have done some phenomenal step around how to manage this complexity. But there are customers who have a lesser complex cloud which is, they don't go to Alibaba, they don't have footprint in all three clouds. So their multi-cloud footprint could be a bit more manageable, but still struggle with a lot of the same problems around cost, around security, around talent. Talent is a big thing, right? And in Suresh's case I think it's slightly more exasperated because every cloud provider Be it AWS, JCP, or Azure brings in hundreds of services and there is nobody, including many of us, right? We learn every day, nowadays, right? It's not that there is one service integrator who knows all, while technically people can claim as a part of sales. But in reality all of us are continuing to learn in this landscape. And if you put all of this equation together with multiple clouds the complexity just starts to exponentially grow. And that's exactly what I think JCI is experiencing and Suresh's team has been experiencing, and we've been working together. But the common problems are around security talent and cost management of this, right? Those are my three things. And one last thing that I would love to say before we move away from this question is, if you think about cloud operations as a concept that's evolving over the last few years, and I have touched upon this in the previous episode as well, Lisa, right? If you take architectures, we've gone into microservices, we've gone into all these server-less architectures all the fancy things that we want. That helps us go to market faster, be more competent to as a business. But that's not simplified stuff, right? That's complicated stuff. It's a lot more distributed. Second, again, we've advanced and created more modern infrastructure because all of what we are talking is platform as a service, services on the cloud that we are consuming, right? In the same case with development we've moved into a DevOps model. We kind of click a button put some code in a repository, the code starts to run in production within a minute, everything else is automated. But then when we get to operations we are still stuck in a very old way of looking at cloud as an infrastructure, right? So you've got an infra team, you've got an app team, you've got an incident management team, you've got a soft knock, everything. But again, so Suresh can talk about this more because they are making significant strides in thinking about this as a single workload, and how do I apply engineering to go manage this? Because a lot of it is codified, right? So automation. Anyway, so that's kind of where the complexity is and how we are thinking, including JCI as a partner thinking about taming that complexity as we move forward. >> Suresh, let's talk about that taming the complexity. You guys have both done a great job of articulating the ostensible challenges that are there with cloud, especially multi-cloud environments that you're living in. But Suresh, talk about the partnership with Hitachi Vantara. How is it helping to dial down some of those inherent complexities? >> I mean, I always, you know, I think I've said this to Prem multiple times. I treat my partners as my internal, you know, employees. I look at Prem as my coworker or my peers. So the reason for that is I want Prem to have the same vested interest as a partner in my success or JCI success and vice versa, isn't it? I think that's how we operate and that's how we have been operating. And I think I would like to thank Prem and Hitachi Vantara for that really been an amazing partnership. And as he was saying, we have taken a completely holistic approach to how we want to really be in the market and play in the market to our customers. So if you look at my jacket it talks about OpenBlue platform. This is what JCI is building, that we are building this OpenBlue digital platform. And within that, my team, along with Prem's or Hitachi's, we have built what we call as Polaris. It's a technical platform where our apps can run. And this platform is automated end-to-end from a platform engineering standpoint. We stood up a platform engineering organization, a reliability engineering organization, as well as a support organization where Hitachi played a role. As I said previously, you know, for me to scale I'm not going to really have the talent and the knowledge of every function that I'm looking at. And Hitachi, not only they brought the talent but they also brought what he was talking about, Harc. You know, they have set up a lot and now we can leverage it. And they also came up with some really interesting concepts. I went and met them in India. They came up with this concept called IPL. Okay, what is that? They really challenged all their employees that's working for GCI to come up with innovative ideas to solve problems proactively, which is self-healing. You know, how you do that? So I think partners, you know, if they become really vested in your interests, they can do wonders for you. And I think in this case Hitachi is really working very well for us and in many aspects. And I'm leveraging them... You started with support, now I'm leveraging them in the automation, the platform engineering, as well as in the reliability engineering and then in even in the engineering spaces. And that like, they are my end-to-end partner right now? >> So you're really taking that holistic approach that you talked about and it sounds like it's a very collaborative two-way street partnership. Prem, I want to go back to, Suresh mentioned Harc. Talk a little bit about what Harc is and then how partners fit into Hitachi's Harc strategy. >> Great, so let me spend like a few seconds on what Harc is. Lisa, again, I know we've been using the term. Harc stands for Hitachi application reliability sectors. Now the reason we thought about Harc was, like I said in the beginning of this segment, there is an illusion from an architecture standpoint to be more modern, microservices, server-less, reactive architecture, so on and so forth. There is an illusion in your development methodology from Waterfall to agile, to DevOps to lean, agile to path program, whatever, right? Extreme program, so on and so forth. There is an evolution in the space of infrastructure from a point where you were buying these huge humongous servers and putting it in your data center to a point where people don't even see servers anymore, right? You buy it, by a click of a button you don't know the size of it. All you know is a, it's (indistinct) whatever that name means. Let's go provision it on the fly, get go, get your work done, right? When all of this is advanced when you think about operations people have been solving the problem the way they've been solving it 20 years back, right? That's the issue. And Harc was conceived exactly to fix that particular problem, to think about a modern way of operating a modern workload, right? That's exactly what Harc. So it brings together finest engineering talent. So the teams are trained in specific ways of working. We've invested and implemented some of the IP, we work with the best of the breed partner ecosystem, and I'll talk about that in a minute. And we've got these facilities in Dallas and I am talking from my office in Dallas, which is a Harc facility in the US from where we deliver for our customers. And then back in Hyderabad, we've got one more that we opened and these are facilities from where we deliver Harc services for our customers as well, right? And then we are expanding it in Japan and Portugal as we move into 23. That's kind of the plan that we are thinking through. However, that's what Harc is, Lisa, right? That's our solution to this cloud complexity problem. Right? >> Got it, and it sounds like it's going quite global, which is fantastic. So Suresh, I want to have you expand a bit on the partnership, the partner ecosystem and the role that it plays. You talked about it a little bit but what role does the partner ecosystem play in really helping JCI to dial down some of those challenges and the inherent complexities that we talked about? >> Yeah, sure. I think partners play a major role and JCI is very, very good at it. I mean, I've joined JCI 18 months ago, JCI leverages partners pretty extensively. As I said, I leverage Hitachi for my, you know, A group and the (indistinct) space and the cloud operations space, and they're my primary partner. But at the same time, we leverage many other partners. Well, you know, Accenture, SCL, and even on the tooling side we use Datadog and (indistinct). All these guys are major partners of our because the way we like to pick partners is based on our vision and where we want to go. And pick the right partner who's going to really, you know make you successful by investing their resources in you. And what I mean by that is when you have a partner, partner knows exactly what kind of skillset is needed for this customer, for them to really be successful. As I said earlier, we cannot really get all the skillset that we need, we rely on the partners and partners bring the the right skillset, they can scale. I can tell Prem tomorrow, "Hey, I need two parts by next week", and I guarantee it he's going to bring two parts to me. So they let you scale, they let you move fast. And I'm a big believer, in today's day and age, to get things done fast and be more agile. I'm not worried about failure, but for me moving fast is very, very important. And partners really do a very good job bringing that. But I think then they also really make you think, isn't it? Because one thing I like about partners they make you innovate whether they know it or not but they do because, you know, they will come and ask you questions about, "Hey, tell me why you are doing this. Can I review your architecture?" You know, and then they will try to really say I don't think this is going to work. Because they work with so many different clients, not JCI, they bring all that expertise and that's what I look from them, you know, just not, you know, do a T&M job for me. I ask you to do this go... They just bring more than that. That's how I pick my partners. And that's how, you know, Hitachi's Vantara is definitely one of a good partner from that sense because they bring a lot more innovation to the table and I appreciate about that. >> It sounds like, it sounds like a flywheel of innovation. >> Yeah. >> I love that. Last question for both of you, which we're almost out of time here, Prem, I want to go back to you. So I'm a partner, I'm planning on redefining CloudOps at my company. What are the two things you want me to remember from Hitachi Vantara's perspective? >> So before I get to that question, Lisa, the partners that we work with are slightly different from from the partners that, again, there are some similar partners. There are some different partners, right? For example, we pick and choose especially in the Harc space, we pick and choose partners that are more future focused, right? We don't care if they are huge companies or small companies. We go after companies that are future focused that are really, really nimble and can change for our customers need because it's not our need, right? When I pick partners for Harc my ultimate endeavor is to ensure, in this case because we've got (indistinct) GCI on, we are able to operate (indistinct) with the level of satisfaction above and beyond that they're expecting from us. And whatever I don't have I need to get from my partners so that I bring this solution to Suresh. As opposed to bringing a whole lot of people and making them stand in front of Suresh. So that's how I think about partners. What do I want them to do from, and we've always done this so we do workshops with our partners. We just don't go by tools. When we say we are partnering with X, Y, Z, we do workshops with them and we say, this is how we are thinking. Either you build it in your roadmap that helps us leverage you, continue to leverage you. And we do have minimal investments where we fix gaps. We're building some utilities for us to deliver the best service to our customers. And our intention is not to build a product to compete with our partner. Our intention is to just fill the wide space until they go build it into their product suite that we can then leverage it for our customers. So always think about end customers and how can we make it easy for them? Because for all the tool vendors out there seeing this and wanting to partner with Hitachi the biggest thing is tools sprawl, especially on the cloud is very real. For every problem on the cloud. I have a billion tools that are being thrown at me as Suresh if I'm putting my installation and it's not easy at all. It's so confusing. >> Yeah. >> So that's what we want. We want people to simplify that landscape for our end customers, and we are looking at partners that are thinking through the simplification not just making money. >> That makes perfect sense. There really is a very strong symbiosis it sounds like, in the partner ecosystem. And there's a lot of enablement that goes on back and forth it sounds like as well, which is really, to your point it's all about the end customers and what they're expecting. Suresh, last question for you is which is the same one, if I'm a partner what are the things that you want me to consider as I'm planning to redefine CloudOps at my company? >> I'll keep it simple. In my view, I mean, we've touched upon it in multiple facets in this interview about that, the three things. First and foremost, reliability. You know, in today's day and age my products has to be reliable, available and, you know, make sure that the customer's happy with what they're really dealing with, number one. Number two, my product has to be secure. Security is super, super important, okay? And number three, I need to really make sure my customers are getting the value so I keep my cost low. So these three is what I would focus and what I expect from my partners. >> Great advice, guys. Thank you so much for talking through this with me and really showing the audience how strong the partnership is between Hitachi Vantara and JCI. What you're doing together, we'll have to talk to you again to see where things go but we really appreciate your insights and your perspectives. Thank you. >> Thank you, Lisa. >> Thanks Lisa, thanks for having us. >> My pleasure. For my guests, I'm Lisa Martin. Thank you so much for watching. (soothing music)

>>Okay. We're here with the rest of the child who was the chief product officer at Altryx and Adam Wilson, the CEO of Trifacta. Now of course, part of Altryx just closed this quarter. Gentlemen. Welcome. >>Great to be here. >>Okay. So rest, let me start with you. In my opening remarks, I talked about Altrix is traditional position serving business analysts and how the hyper Anna acquisition brought you deeper into the business user space. What does Trifacta bring to your portfolio? Why'd you buy the company? >>Yeah. Thank you. Thank you for the question. Um, you know, we see, uh, we see a massive opportunity of helping, um, brands, um, democratize the use of analytics across their business. Um, every knowledge worker, every individual in the company should have access to analytics. It's no longer optional, um, as they navigate, uh, their businesses with that in mind, you know, we know designer and are the products that Ultrix has been selling the past decade or so do a really great job, um, addressing the business analysts, uh, with, um, hyperaware, um, now kind of renamed, um, Altrix auto insights. Uh, we even speak with the, uh, business owner of the line of business owner. Who's looking for insights that aren't real in traditional dashboards and so on. Um, but we see this opportunity of really helping the data engineering teams and it organizations, um, to also make better use of analytics. Um, and that's where the drive factor comes in for us. Um, drive factor has the best data engineering cloud in the planet. Um, they have an established track record of working across multiple cloud platforms and helping data engineers, um, do better data pipelining and work better with, uh, this massive kind of cloud transformation that's happening in every business. Um, and so Trifacta made so much sense for us. >>Yeah. Thank you for that. I mean, look, you could have built it yourself. Would've taken, you know, who knows how long, but, uh, so definitely a great time to market move, Adam. I wonder if we could dig into Trifacta some more, I mean, I remember interviewing Joe Hellerstein in the early days. You've talked about this as well, uh, on the cube coming at the problem of taking data from raw refined to an experience point of view. And Joe in the early days, talked about flipping the model and starting with data visualization, something Jeff, her was expert at. So maybe explain how we got here. We used to have this cumbersome process of ETL and you may be in some others changed that model with ELL and then T explain how Trifacta really changed the data engineering game. >>Yeah, that's exactly right. Uh, David, it's been a really interesting journey for us because I think the original hypothesis coming out of the campus research, uh, at Berkeley and Stanford that really birthed Trifacta was, you know, why is it that the people who know the data best can't do the work? You know, why is this become the exclusive purview of the highly technical and, you know, can we rethink this and make this a user experience, problem powered by machine learning that will take some of the more complicated things that people want to do with data and really helped to automate those. So, so a, a broader set of users can, um, can really see for themselves and help themselves. And, and I think that, um, there was a lot of pent up frustration out there because people have been told for, you know, for a decade now to be more data-driven and then the whole time they're saying, well, then give me the data, you know, in the shape that I can use it with the right level of quality and I'm happy to be, but don't tell me to be more data driven and then, and, and not empower me, um, to, to get in there and to actually start to work with the data in meaningful ways. >>And so, um, that was really, you know, what, you know, the origin story of the company. And I think as, as we, um, you know, saw over the course of the last 5, 6, 7 years that, um, you know, a real, uh, excitement to embrace this idea of, of trying to think about data engineering differently, trying to democratize the, the ETL process and to also leverage all of these exciting new, uh, engines and platforms that are out there that allow for processing, you know, ever more diverse data sets, ever larger data sets and new and interesting ways. And that's where a lot of the push down or the ELT approaches that, you know, I think it could really won the day. Um, and that, and that for us was a hallmark of the solution from the very beginning. >>Yeah, this is a huge point that you're making. This is first of all, there's a large business, it's probably about a hundred billion dollar Tam. Uh, and the, the point you're making is we've looked, we've contextualized most of our operational systems, but the big data pipelines hasn't gotten there. And maybe we could talk about that a little bit because democratizing data is Nirvana, but it's been historically very difficult. You've got a number of companies it's very fragmented and they're all trying to attack their little piece of the problem to achieve an outcome, but it's been hard. And so what's going to be different about Altryx as you bring these puzzle pieces together, how is this going to impact your customers who would like to take that one? >>Yeah, maybe, maybe I'll take a crack at it. And Adam will, um, add on, um, you know, there hasn't been a single platform, uh, for analytics automation in the enterprise, right? People have relied on, uh, different products, um, to solve kind of, uh, smaller problems, um, across this analytics, automation, data transformation domain. Um, and, um, I think uniquely altereds has that opportunity. Uh, we've got 7,000 plus customers who rely on analytics for, um, data management, for analytics or AI and ML, uh, for transformations, uh, for reporting and visualization for automated insights and so on. And so by bringing drive factor, we have the opportunity to scale this even further and solve for more use cases, expand the scenarios where it's gets applied and so multiple personas. Um, and now we just talked about the data engineers. They are really a growing stakeholder in this transformation of data and analytics. >>Yeah, good. Maybe we can stay on this for a minute cause you, you you're right. You bring it together. Now that at least 3% is the business analyst, the end user slash business user. And now the data engineer, which is really out of an it role in a lot of companies, and you've used this term, the data engineering cloud, what is that, how is it going to integrate in with, or support these other personas? And, and how's it going to integrate into the broader ecosystem of clouds and cloud data warehouses or any other data stores? >>Yeah, no, that's great. Uh, yeah, I think for us, we really looked at this and said, you know, we want to build an open and interactive cloud platform for data engineers, you know, to collaboratively profile pipeline, um, and prepare data for analysis. And that really meant collaborating with the analysts that were in the line of business. And so this is why a big reason why this combination is so magic because ultimately if we can get the data engineers that are creating the data products together with the analysts that are, uh, in the line of business that are driving a lot of the decision-making and allow for that, what I would describe as collaborative curation of the data together, so that you're starting to see, um, uh, you know, increasing returns to scale as this, uh, as this rolls out. I just think that is an incredibly powerful combination and, and frankly, something that the market has not cracked the code on yet. And so, um, I think when we, when I sat down with Suresh and with mark and the team at Ultrix, that was really part of the, the, the big idea, the big vision that that was painted and, and got us really energized about the acquisition and about the potential of the combination. >>Yeah. And you're really, you're obviously riding the cloud and the cloud native wave. Um, and, but specifically we're seeing, you know, I almost don't even want to call it a data warehouse anyway, because when you look at what's, for instance, snowflake is doing, of course their marketing is around the data cloud, but I actually think there's real justification for that because it's not like the traditional data warehouse, right. It's, it's simplified get there fast, don't necessarily have to go through the central organization to share data. Uh, and, and, and, but it's really all about simplification, right? Isn't that really what the democratization comes down to. >>Yeah. It's simplification and collaboration. Right. I don't want to, I want to kind of just, um, what Adam said resonates with me deeply, um, analytics is one of those, um, massive disciplines, an enterprise that's really had the weakest of tools. Um, and we just have interfaces to collaborate with, and I think truly this was Alteryx's and a superpower was helping the analysts get more out of their data, get more out of the analytics, like imagine a world where these people are collaborating and sharing insights in real time and sharing workflows and getting access to new data sources, um, understanding data models better, I think, um, uh, curating those insights. I boring Adam's phrase again. Um, I think that creates a real value inside the organization, uh, because frankly in scaling analytics and democratizing analytics and data, we're still in such early phases of this journey. >>So how should we think about designer cloud, which is from Altryx it's really been the on-prem and the server desktop offering. And of course Trifacta is with cloud cloud data warehouses. Right. Uh, how, how should we think about those two products? >>Yeah, I think, I think you should think about them and, uh, um, as, as very complimentary right design a cloud really shares a lot of DNA and heritage with, uh, designer desktop, um, the low code tooling and that interface, uh, that really appeals to the business analysts, um, and gets a lot of the things that they do well, we've also built it with interoperability in mind, right. So if you started building your workflows in designer desktop, you want to share that with design and cloud, we want to make it super easy for you to do that. Um, and I think over time now we're only a week into, um, this Alliance with, um, with Trifacta. Um, I think we have to get deeper inside to think about what does the data engineer really need what's business analysts really need and how to design a cloud, and Trifacta really support both of those requirements, uh, while kind of continue to build on the tri-factor on the amazing tri-factor cloud platform. >>You know, >>I was just going to say, I think that's one of the things that, um, you know, creates a lot of, uh, opportunity as we go forward, because ultimately, you know, Trifacta took a platform, uh, first mentality to everything that we built. So thinking about openness and extensibility and, um, and how over time people could build things on top of, by factor that are a variety of analytic tool chain, or analytic applications. And so, uh, when you think about, um, Ultrix now starting to, uh, to move some of its capabilities or to provide additional capabilities, uh, in the cloud, um, you know, Trifacta becomes a platform that can accelerate, you know, all of that work and create, uh, uh, a cohesive set of, of cloud-based services that, um, share a common platform. And that maintains independence because both companies, um, have been, uh, you know, fiercely independent, uh, and really giving people choice. >>Um, so making sure that whether you're, uh, you know, picking one cloud platform and other, whether you're running things on the desktop, uh, whether you're running in hybrid environments, that, um, no matter what your decision, um, you're always in a position to be able to get out your data. You're always in a position to be able to cleanse transform shape structure, that data, and ultimately to deliver, uh, the analytics that you need. And so I think in that sense, um, uh, you know, this, this again is another reason why the combination, you know, fits so well together, giving people, um, the choice. Um, and as they, as they think about their analytics strategy and their platform strategy going forward, >>Yeah. I make a chuckle, but I, one of the reasons I always liked Altryx is cause you kinda did the little end run on it. It can be a blocker sometimes, but that created problems, right? Because the current organization said, wow, there's big data stuff is taken off, but we need security. We need governance. And, and it was interesting because he got, you know, ETTL has been complex, whereas the visualization tools, they really, you know, really weren't great at governance and security. It took some time there. So that's not, not their heritage. You're bringing those worlds together. And I'm interested, you guys just had your sales kickoff, you know, what was their reaction like, uh, maybe Suresh, you could start off and maybe Adam, you could bring us home. >>Yeah. Um, thanks for asking about our sales kickoff. So we met for the first time and kind of two years, right. For, as, as it is for many of us, um, in person, uh, um, which I think was, uh, was a real breakthrough as Qualtrics has been on its transformation journey. Uh, we had a Trifacta to, um, the, the party such as the tour, um, and getting all of our sales teams and product organizations, um, to meet in person in one location. I thought that was very powerful for us, the company. Uh, but then I tell you, um, um, the reception for Trifacta was beyond anything I could have imagined. Uh, we were working Adam and I were working so hard on, on the deal and the core hypothesis and so on. And then you step back and you kind of share the vision, uh, with the field organization and it blows you away, the energy that it creates among our sellers, our partners, and I'm sure Adam will, and his team were mocked every single day with questions and opportunities to bring them in. >>But Adam, maybe he's chair. Yeah, I know it was, uh, it was through the roof. I mean, uh, uh, the, uh, the amount of energy, the, uh, certainly how welcoming everybody was, uh, uh, you know, just, I think the story makes so much sense together. I think culturally, the company is, are very aligned. Um, and, uh, it was a real, uh, real capstone moment, uh, to be able to complete the acquisition and to, and to close and announced, you know, at the kickoff event. And, um, I think, you know, for us, when we really thought about it, you know, when we ended the story, that we was just, you have this opportunity to really cater to what the end-users, you know, care about, which is a lot about interactivity and self-service, and at the same time. And that's, and that's a lot of the goodness that, um, that Ultrix has brought, you know, through, you know, you know, years and years of, of building a very vibrant community of, you know, thousands, hundreds of thousands of users. >>And on the other side, you know, Trifacta bringing in this data engineering focus, that's really about, uh, the governance things that you mentioned and the openness, um, that, that it cares deeply about. And all of a sudden, now you have a chance to put that together into a complete story where the data engineering cloud and analytics, automation, you know, coming together. And, um, and I just think, you know, the lights went on, um, you know, for people instantaneously and, you know, this is a story that, um, that I think the market is really hungry for. And certainly the reception we got from, uh, from the broader team at kickoff was, uh, was a great indication of that. >>Well, I think the story hangs together really well, you know, one of the better ones I've seen in, in this space, um, and, and you guys coming off a really, really strong quarter. So congratulations on that Jensen. We have to leave it there. I really appreciate your time today. Yeah. Take a look at this short video. And when we come back, we're going to dig into the ecosystem and the integration into cloud data warehouses and how leading organizations are creating modern data teams and accelerating their digital businesses. You're watching the cube, your leader in enterprise tech coverage.

[Music] okay we're here with ceres vitale who's the chief product officer at alteryx and adam wilson the ceo of trifacta now of course part of alteryx just closed this quarter gentlemen welcome great to be here okay so rush let me start with you in my opening remarks i talked about alteryx's traditional position serving business analysts and how the hyperanna acquisition brought you deeper into the business user space what does trifacta bring to your portfolio why'd you buy the company yeah thank you thank you for the question um you know we see a we see a massive opportunity of helping brands democratize the use of analytics across their business every knowledge worker every individual in the company should have access to analytics it's no longer optional as they navigate their businesses with that in mind you know we know designer and our the products that alteryx has been selling the past decade or so do a really great job addressing the business analysts with hyper rana now kind of renamed alteryx auto insights we even speak with the business owner the line of business owner who's looking for insights that aren't revealed in traditional dashboards and so on um but we see this opportunity of really helping the data engineering teams and i.t organizations to also make better use of analytics and that's where trifacta comes in for us trifacta has the best data engineering cloud in the planet they have an established track record of working across multiple cloud platforms and helping data engineers um do better data pipelining and work better with this massive kind of cloud transformation that's happening in every business um and so trifecta made so much sense for us yeah thank you for that i mean look you could have built it yourself would have taken you know who knows how long you know but uh so definitely a great time to market move adam i wonder if we could dig into trifacta some more i mean i remember interviewing joe hellerstein in the early days you've talked about this as well on thecube coming at the problem of taking data from raw refined to an experience point of view and joe in the early days talked about flipping the model and starting with data visualization something jeff herr was expert at so maybe explain how we got here we used to have this cumbersome process of etl and you maybe and some others change that model with you know el and then t explain how trifacta really changed the data engineering game yeah that's exactly right uh dave and it's been a really interesting journey for us because i think the original hypothesis coming out of the campus research at berkeley and stanford that really birthed trifacta was you know why is it that the people who know the data best can't do the work you know why is this become the exclusive purview the highly technical and you know can we rethink this and make this a user experience problem powered by machine learning that will take some of the more complicated things that people want to do with data and really help to automate those so so a broader set of users can can really see for themselves and help themselves and and i think that um there was a lot of pent up frustration out there because people have been told for you know for a decade now to be more data driven and then the whole time they're saying well then give me the data you know in the shape that i can use it with the right level of quality and i'm happy to be but don't tell me to be more data driven and they'll don't then and and not empower me um to to get in there and to actually start to work with the data in meaningful ways and so um that was really you know what you know the origin story of the company and i think as as we saw over the course of the last five six seven years that um you know a real uh excitement to embrace this idea of of trying to think about data engineering differently trying to democratize the the etl process and to also leverage all these exciting new uh engines and platforms that are out there that allow for you know processing you know ever more diverse data sets ever larger data sets and new and interesting ways and that's where a lot of the push down or the elt approaches uh you know i think it really won the day um and that and that for us was a hallmark of the solution from the very beginning yeah this is a huge point that you're making this is first of all there's a large business probably about a hundred billion dollar tam uh and and the the point you're making is we look we've contextualized most of our operational systems but the big data pipelines hasn't gotten there but and maybe we could talk about that a little bit because democratizing data is nirvana but it's been historically very difficult you've got a number of companies it's very fragmented and they're all trying to attack their little piece of the problem to achieve an outcome but it's been hard and so what's going to be different about alteryx as you bring these puzzle pieces together how is this going to impact your customers who would like to take that one yeah maybe maybe i'll take a crack at it and adam will add on um you know there hasn't been a single platform [Music] for analytics automation in the enterprise right people have relied on different products to solve kind of smaller problems across this analytics automation data transformation domain and i think uniquely alteryx has that opportunity we've got 7000 plus customers who rely on analytics for data management for analytics for ai and ml for transformations for reporting and visualization for automated insights and so on and so by bringing trifecta we have the opportunity to scale this even further and solve for more use cases expand the scenarios where angles gets applied and serve multiple personas um and now we just talked about the data engineers they are really a growing stakeholder in this transformation of data analytics yeah good maybe we can stay on this for a minute because you're right you bring it together now at least three personas the business analyst the end user size business user and now the data engineer which is really out of an i.t role in a lot of companies and you've used this term the data engineering cloud what is that how is it going to integrate in with or support these other personas and and how's it going to integrate into the broader ecosystem of clouds and cloud data warehouses or any other data stores yeah you know that's great uh you know i think for us we really looked at this and said you know we want to build an open and interactive you know cloud platform for data engineers you know to collaboratively profile pipeline um and prepare data for analysis and and that really meant collaborating with the analysts that were in the line of business and so this is why a big reason why this combination is so magic because ultimately if we can get the data engineers that are creating the data products together with the analysts that are in the line of business that are driving a lot of the decision making and allow for that what i would describe as collaborative curation you know of the data together so that you're starting to see um uh you know increasing returns to scale as this uh as this rolls out i just think that is an incredibly uh powerful combination and frankly something that the market has not cracked the code on yet and so um i think when we when i sat down with surash and with mark and and the team at ultrix that was really part of the the big idea the big vision that that was painted and and got us really energized um about the acquisition and about the the potential of the combination yeah and you're really you're obviously riding the cloud and the cloud native wave um and but specifically we're seeing you know i almost don't even want to call it a data warehouse anyway because when you look at what princeton snowflake is doing of course their marketing is around the data cloud but i i actually think there's real justification for that because it's not like the traditional data warehouse right it's it's simplified get there fast don't necessarily have to go through this central organization to share data uh and and but it's really all about simplification right isn't that really what the democratization comes down to yeah it's simplification and collaboration right i don't want to i want to kind of just uh what what adam said resonates with me deeply um analytics is one of those massive disciplines inside an enterprise that's really had the weakest of tools um and weakest of interfaces to collaborate with and i think truly this was alteryx's end of superpower was helping the analysts get more out of their data get more out of the analytics like imagine a world where these people are collaborating and sharing insights in real time and sharing workflows and getting access to new data sources understanding data models better i think curating those insights i borrowing adam's phrase again i think that creates a real value inside the organization because frankly in scaling analytics and democratizing analytics and data we're still in such early phases of this journey so how should we think about designer cloud which is from alteryx it's really been the on-prem the server or desktop you know offering and of course trifecta is about cloud cloud data warehouses right um how should we think about those two products yeah i think i think you should think about them and as very complementary right designer cloud really shares a lot of dna and heritage with designer desktop the low code tooling and the interface that really appeals to the business analysts and gets a lot of the things that they do well we've also built it with interoperability in mind right so if you started building your workflows in designer desktop you want to share that with designer cloud we want to make it super easy for you to do that and i think over time now we're only a week into this alliance with uh with trifacta i think we have to get deeper and start to think about what does the data engineer really need what business analysts really need and how to design a cloud and try factor really support both of those requirements uh while kind of continue to build on the trifecta on the amazing trifecta cloud platform you know and i think let's go ahead i'm just to say i think that's one of the things that um you know creates a lot of opportunity as we go forward because ultimately you know trifacta took a platform uh first mentality to everything that we built so thinking about openness and extensibility and um and how over time people could build things on top of trifacta that are a variety of analytic tool chain or analytic applications and so when you think about um alteryx now starting to uh to move some of its capabilities or to provide additional capabilities uh in the cloud um you know trifacta becomes uh a a platform that can accelerate you know all of that work and create a cohesive set of of cloud-based services that share a common platform and that maintains independence because both companies um have been uh you know fiercely independent uh in really giving people choice um so making sure that whether you're uh you know picking one cloud platform another whether you're running things on the desktop uh whether you're running in hybrid environments that no matter what your decision you're always in a position to be able to get out your data you're always in a position to be able to cleanse transform shape structure that data and ultimately to deliver uh the analytics that you need and so i think in in that sense um uh you know this this again is another reason why the combination you know fits so well together giving people um the choice um and as they as they think about their analytics strategy and and their platform strategy going forward you know i make a chuckle but one of the reasons i always liked alteryx is because you kind of did did a little end run on i.t i.t can be a blocker sometimes but that created problems right because the organization said wow this big data stuff is taken off but we need security we need governance and and it's interesting because you got you know etl has been complex whereas the visualization tools they really you know really weren't great at governance and security it took some time there so that's not not their heritage you're bringing those worlds together and i'm interested you guys just had your sales kickoff you know what was the reaction like uh maybe suresh you could start off and maybe adam you could bring us home yeah um thanks for asking about our sales kickoff so we met uh for the first time in kind of two years right for as it is for many of us um in person uh um which i think was a was a real breakthrough as alteryx has been on its transformation journey uh we had a try factor to um the the party such as it were um and getting all of our sales teams and product organizations um to meet in person in one location i thought that was very powerful for us as a company but then i tell you um the reception for trifecta was beyond anything i could have imagined uh we were working adam and i were working so hard on on the the deal and the core hypotheses and so on and then you step back and kind of share the vision with the field organization and it blows you away the energy that it creates among our sellers our partners and i'm sure adam and his team were mobbed every single day with questions and opportunities to bring them in but adam maybe you should share yeah no it was uh it was through the roof i mean uh the uh the amount of energy the uh when so certainly how welcoming everybody was uh you know just i think the story makes so much sense together i think culturally the companies are very aligned um and uh it was a real uh real capstone moment uh to be able to complete the acquisition and to and to close and announce you know at the kickoff event and um i think you know for us when we really thought about it you know when we and the story that we told was just you have this opportunity to really cater to what the end users you know care about which is a lot about interactivity and self-service and at the same time and that's and that's a lot of the goodness that um that alteryx is has brought you know through you know you know years and years of of building a very vibrant community of you know thousands hundreds of thousands of users and on the other side you know trifecta bringing in this data engineering focus that's really about uh the governance things that you mentioned and the openness that that it cares deeply about and all of a sudden now you have a chance to put that together into a complete story where the data engineering cloud and analytics automation you know come together and um and i just think you know the lights went on um you know for people instantaneously and you know this is a story that um that i think the market is really hungry for and and certainly the reception we got from from the broader team at kickoff was uh was a great indication of that well i think the story hangs together really well you know one of the better ones i've seen in this space um and and you guys coming off a really really strong quarter so congratulations on that gents we have to leave it there really appreciate your time today yeah take a look at this short video and when we come back we're going to dig into the ecosystem and the integration into cloud data warehouses and how leading organizations are creating modern data teams and accelerating their digital businesses you're watching the cube your leader in enterprise tech coverage [Music]

>>from around the globe. It's the Cube presenting enterprise, Digital resilience on hybrid and multi cloud Brought to You by Iota Ho. Okay, let's now get into the next segment where we'll explore data automation. But from the angle of digital resilience within and as a service consumption model, we're now joined by Yusuf Khan, who heads data services for Iota Ho and Shirish County. Up in Who's the vice president and head of U. S. Sales at happiest Minds. Gents, welcome to the program. Great to have you in the Cube. >>Thank you, David. >>Stretch. You guys talk about happiest minds. This notion of born digital, foreign agile. I like that. But talk about your mission at the company. >>Sure. A former in 2011 Happiest minds Up Born digital born a child company. >>The >>reason is that we are focused on customers. Our customer centric approach on delivering digitals and seamless solutions have helped us be in the race. Along with the Tier one providers, our mission, happiest people, happiest customers is focused to enable customer happiness through people happiness. We have Bean ranked among the top 25 I t services company in the great places to work serving hour glass to ratings off 4.1 against the rating off five is among the job in the Indian nineties services company that >>shows the >>mission on the culture. What we have built on the values, right sharing, mindful, integrity, learning and social on social responsibilities are the core values off our company on. That's where the entire culture of the company has been built. >>That's great. That sounds like a happy place to be. Now you have you head up data services for Iot Tahoe. We've talked in the past. Of course you're out of London. What do you what's your day to day focus with customers and partners? What you focused on? >>Well, David, my team work daily with customers and partners to help them better understand their data, improve their data quality, their data governance on help them make that data more accessible in a self service kind of way. To the stakeholders within those businesses on dis is all a key part of digital resilience that will will come on to talk about but later. You're >>right, e mean, that self service theme is something that we're gonna we're gonna really accelerate this decade, Yussef and so. But I wonder before we get into that, maybe you could talk about the nature of the partnership with happiest minds. You know, why do you guys choose toe work closely together? >>Very good question. Um, we see Io Tahoe on Happiest minds as a great mutual fit. A Suresh has said happiest minds are very agile organization. Um, I think that's one of the key things that attracts their customers on Io. Tahoe is all about automation. We're using machine learning algorithms to make data discovery data cataloging, understanding, data, redundancy, uh, much easier on. We're enabling customers and partners to do it much more quickly. So when you combine our emphasis on automation with the emphasis on agility, the happiest minds have that. That's a really nice combination. Work works very well together, very powerful. I think the other things that a key are both businesses, a serious have said are really innovative digital native type type companies. Um, very focused on newer technologies, the cloud etcetera, uh, on. Then finally, I think that both challenger brands Andi happiest minds have a really positive, fresh ethical approach to people and customers that really resonates with us that I have tied to its >>great thank you for that. So Russia, Let's get into the whole notion of digital resilience. I wanna I wanna sort of set it up with what I see. And maybe you can comment be prior to the pandemic. A lot of customers that kind of equated disaster recovery with their business continuance or business resilient strategy, and that's changed almost overnight. How have you seen your clients respond to that? What? I sometimes called the forced march to become a digital business. And maybe you could talk about some of the challenges that they faced along the way. >>Absolutely. So, uh, especially during this pandemic times when you see Dave customers have been having tough times managing their business. So happiest minds. Being a digital Brazilian company, we were able to react much faster in the industry, apart from the other services company. So one of the key things is the organizations trying to adopt onto the digital technologies right there has bean lot off data which has been to managed by these customers on. There have been lot off threats and risk, which has been to manage by the CEO Seo's so happiest minds digital resilient technology fight the where we're bringing the data complaints as a service, we were ableto manage the resilience much ahead off other competitors in the market. We were ableto bring in our business community processes from day one, where we were ableto deliver our services without any interruption to the services what we were delivering to our customers. >>So >>that is where the digital resilience with business community process enabled was very helpful for us who enable our customers continue there business without any interruptions during pandemics. >>So, I mean, some of the challenges that that customers tell me they obviously had to figure out how to get laptops to remote workers and that that whole remote, you know, work from home pivot figure out how to secure the end points. And, you know, those were kind of looking back there kind of table stakes, but it sounds like you've got a digital business means a data business putting data at the core, I like to say, but so I wonder if you could talk a little bit more about maybe the philosophy you have toward digital resilience in the specific approach you take with clients? >>Absolutely. They seen any organization data becomes. The key on this for the first step is to identify the critical data. Right. So we this is 1/6 process. What we following happiest minds. First of all, we take stock off the current state, though the customers think that they have a clear visibility off their data. How are we do more often assessment from an external point off view on See how critical their data is? Then we help the customers to strategies that right the most important thing is to identify the most important critical herself. Data being the most critical assault for any organization. Identification off the data's key for the customers. Then we help in building a viable operating model to ensure these identified critical assets are secure on monitor dearly so that they are consumed well as well as protected from external threats. Then, as 1/4 step, we try to bring in awareness, toe the people we train them at all levels in the organization. That is a P for people to understand the importance off the residual our cells. And then as 1/5 step, we work as a back up plan in terms of bringing in a very comprehensive and the holistic testing approach on people process as well as in technology. We'll see how the organization can withstand during a crisis time. And finally we do a continuous governance off this data, which is a key right. It is not just a one step process. We set up the environment. We do the initial analysis and set up the strategy on continuously govern this data to ensure that they are not only know managed will secure as well as they also have to meet the compliance requirements off the organization's right. That is where we help organizations toe secure on Meet the regulations off the organizations. As for the privacy laws, >>so >>this is a constant process. It's not on one time effort. We do a constant process because every organization goes towards the digital journey on. They have to face all these as part off the evolving environment on digital journey, and that's where they should be kept ready in terms off. No recovering, rebounding on moving forward if things goes wrong. >>So let's stick on that for a minute, and then I wanna bring yourself into the conversation. So you mentioned compliance and governance. When? When your digital business. Here, as you say, you're a data business. So that brings up issues. Data sovereignty. Uh, there's governance, this compliance. There's things like right to be forgotten. There's data privacy, so many things. These were often kind of afterthoughts for businesses that bolted on, if you will. I know a lot of executives are very much concerned that these air built in on, and it's not a one shot deal. So do you have solutions around compliance and governance? Can you deliver that as a service? Maybe you could talk about some of the specifics there, >>so some of way have offered multiple services. Tow our customers on digital race against. On one of the key service is the data complaints. As a service here we help organizations toe map the key data against the data compliance requirements. Some of the features includes in terms off the continuous discovery off data right, because organizations keep adding on data when they move more digital on helping the helping and understanding the actual data in terms off the residents of data, it could be a heterogeneous data sources. It could be on data basis or it could be even on the data lakes. Or it could be or no even on compromise, all the cloud environment. So identifying the data across the various no heterogeneous environment is very key. Feature off our solution. Once we identify, classify this sensitive data, the data privacy regulations on the traveling laws have to be map based on the business rules. So we define those rules on help map those data so that organizations know how critical their digital assets are. Then we work on a continuous marching off data for anomalies because that's one of the key teachers off the solution, which needs to be implemented on the day to day operational basis. So we're helping monitoring those anomalies off data for data quality management on an ongoing basis. And finally we also bringing the automatic data governance where we can manage the sensory data policies on their data relationships in terms off, mapping on manage their business rules on we drive reputations toe also suggest appropriate actions to the customers. Take on those specific data sets. >>Great. Thank you, Yousef. Thanks for being patient. I want to bring in Iota ho thio discussion and understand where your customers and happiest minds can leverage your data automation capability that you and I have talked about in the past. And I'm gonna be great if you had an example is well, but maybe you could pick it up from there. >>Sure. I mean, at a high level, assertions are clearly articulated. Really? Um, Iota, who delivers business agility. So that's by, um, accelerating the time to operationalize data, automating, putting in place controls and ultimately putting, helping put in place digital resilience. I mean, way if we step back a little bit in time, um, traditional resilience in relation to data are often met manually, making multiple copies of the same data. So you have a DB A. They would copy the data to various different places on business. Users would access it in those functional style owes. And of course, what happened was you ended up with lots of different copies off the same data around the enterprise. Very inefficient. Onda course ultimately, uh, increases your risk profile. Your risk of a data breach. Um, it's very hard to know where everything is, and I realized that expression they used David, the idea of the forced march to digital. So with enterprises that are going on this forced march, what they're finding is they don't have a single version of the truth, and almost nobody has an accurate view of where their critical data is. Then you have containers bond with containers that enables a big leap forward so you could break applications down into micro services. Updates are available via a P I s. And so you don't have the same need to build and to manage multiple copies of the data. So you have an opportunity to just have a single version of the truth. Then your challenge is, how do you deal with these large legacy data states that the service has been referring Thio, where you you have toe consolidate, and that's really where I Tahoe comes in. Um, we massively accelerate that process of putting in a single version of the truth into place. So by automatically discovering the data, um, discovering what's duplicate what's redundant, that means you can consolidate it down to a single trusted version much more quickly. We've seen many customers have tried to do this manually, and it's literally taken years using manual methods to cover even a small percentage of their I T estates with a tire. You could do it really very quickly on you can have tangible results within weeks and months. Um, and then you can apply controls to the data based on context. So who's the user? What's the content? What's the use case? Things like data quality validations or access permissions on. Then once you've done there, your applications and your enterprise are much more secure, much more resilient. As a result, you've got to do these things whilst retaining agility, though. So coming full circle. This is where the partnership with happiest minds really comes in as well. You've got to be agile. You've gotta have controls, um, on you've got a drug towards the business outcomes and it's doing those three things together that really deliver for the customer. Thank >>you. Use f. I mean you and I. In previous episodes, we've looked in detail at the business case. You were just talking about the manual labor involved. We know that you can't scale, but also there's that compression of time. Thio get to the next step in terms of ultimately getting to the outcome and we talked to a number of customers in the Cube. And the conclusion is really consistent that if you could accelerate the time to value, that's the key driver reducing complexity, automating and getting to insights faster. That's where you see telephone numbers in terms of business impact. So my question is, where should customers start? I mean, how can they take advantage of some of these opportunities that we've discussed >>today? Well, we've tried to make that easy for customers. So with our Tahoe and happiest minds, you can very quickly do what we call a data health check on. Dis is a is a 2 to 3 weeks process are two Really quickly start to understand and deliver value from your data. Um, so, iota, who deploys into the customer environment? Data doesn't go anywhere. Um, we would look at a few data sources on a sample of data Onda. We can very rapidly demonstrate how date discovery those catalog e understanding Jupiter data and redundant data can be done. Um, using machine learning, um, on how those problems can be solved. Um, and so what we tend to find is that we can very quickly as I say in a matter of a few weeks, show a customer how they could get toe, um, or Brazilian outcome on. Then how they can scale that up, take it into production on, then really understand their data state Better on build resilience into the enterprise. >>Excellent. There you have it. We'll leave it right there. Guys. Great conversation. Thanks so much for coming on the program. Best of luck to you in the partnership. Be well. >>Thank you, David. Sorry. Thank you. Thank >>you for watching everybody, This is Dave Volonte for the Cuban Are ongoing Siris on data Automation without Tahoe.

>> Announcer: From around the globe, It's theCUBE. Presenting Enterprise Digital Resilience on Hybrid and Multicloud. Brought to you by Io-Tahoe. >> Okay, Let's now get into the next segment where we'll explore data automation but from the angle of digital resilience within and as a service consumption model. We're now joined by Yusef Khan, who heads data services for Io-Tahoe and Suresh Kanniappan who's the vice president and head of US sales at Happiest Minds. Gents, welcome to the program, great to have you in theCUBE. >> Thank you, David. >> Suresh, you guys talk about at Happiest Minds this notion of born digital, foreign agile, I like that but talk about your mission at the company. >> Sure, far in 2011, Happiest minds is a born digital, born agile company. The reason is that, we are focused on customers. Our customer centric approach and delivering digital and seamless solutions, have helped us be in the race along with the Tier 1 providers. Our mission, Happiest People, Happiest Customers is focused to enable customer happiness through people happiness. We have been ranked among the top 25 ID services company in the great places to work in service. Our Glassdoor ratings, of four dot one against the rating of five, is among the top in the Indian ID services company, that shows the mission and the culture what we have built on the values, right? Is sharing, mindful, integrity, learning and social responsibilities, are the core values of our company. And that's where the entire culture of the company has been built. >> That's great, sounds like a happy place to be. Now Yusef, you had updated services for Io-Tahoe, we've talked in the past year, of course you're at London. What's your day to day focus with customers and partners? What are you focused on? >> Well David, my team worked daily with customers and partners to help them better understand their data, improve their data quality, their data governance, and help them make that data more accessible in a self-service kind of way to the stakeholders within those businesses. And this is a key part of digital resilience that we allow. We'll come on to talk about a bit later. >> You're right, I mean that self-service theme is something that we're going to really accelerate this decade Yusef. And so, but I wonder before we get into that, maybe you could talk about the nature of the partnership with Happiest Minds, why do you guys choose to work closely together? >> Very good question. We see Io-Tahoe and Happiest Minds as a great mutual fit. As Suresh said, Happiest Minds are a very agile organization. I think that's one of the key things that attracts the customers. And Io-Tahoe is all about automation. We're using machine learning algorithms to make data discovery, data cataloging, understanding data redundancy much easier and we're enabling customers and partners to do it much more quickly. So when you combine our emphasis on automation, with the emphasis on agility that Happiest Minds have. That's a really nice combination, works very well together, very powerful. I think the other things that are key, both businesses as Suresh have said, are really innovative, digital native type companies. Very focused on newer technologies, the cloud, et cetera. And then finally I think they're both challenge brands and Happiest Minds have a really positive, fresh, ethical approach to people and customers that really resonates with us at Io-Tahoe too. >> That's great, thank you for that. Suresh, let's get into the whole notion of digital resilience. I want to sort of set it up with what I see and maybe you can comment. Being prior to the pandemic, a lot of customers that kind of equated disaster recovery with their business continuance or business resilience strategy and that's changed almost overnight. How have you seen your clients respond to that? What I sometimes call the forced match to become a digital business and maybe you could talk about some of the challenges that they've faced along the way. >> Absolutely, So especially during this pandemic times when you see Dave, customers have been having tough times managing their business. So Happiest Minds being a digital resilient company, we were able to react much faster in the industry apart from the other services company. So, one of the key things is, the organizations are trying to adapt onto the digital technologies, right? There has been lot of data which has to be managed by these customers, and there've been a lot of threats and risk which has to be managed by the CIOs. So Happiest Minds Digital Resilient Technology, right? We're bringing the data complaints as a service. We were able to manage the resilience much ahead of other competitors in the market. We were able to bring in our business continuity processes from day one, where we were able to deliver our services without any interruption to the services what we are delivering to our customers. So that is where the digital, the resilience with business continuity process enabled was very helpful for us to enable our customers continue their business without any interruptions during pandemics. >> So, I mean some of the challenges that customers tell me if I may obviously had to figure out how to get laptops to remote workers, that whole remote, work from home pivot, figure out how to secure the end points, and those were kind of looking back they're kind of table stakes. And it sounds like, you got, I mean digital business means, a data business, putting data at the core, I like to say it. But so, I wonder if you could talk a little bit more about, maybe the philosophy you have toward digital resilience and the specific approach you take with clients. >> Absolutely Dave, see in any organization, data becomes the key. And thus for the first step, is to identify the critical data, right? So, this is a six step process plot we follow in Happiest Minds. First of all, we take stock of the current state, though the customers think that they have a clear visibility of their data. However, we do more often assessment from an external point of view and see how critical their data is. Then we help the customers to strategize that, right? The most important thing is to identify the most important critical asset. Data being the most critical asset for any organization, identification of the data are key for the customers. Then we help in building a viable operating model to ensure these identified critical assets are secure and monitored duly so that they are consumed well as well as protected from external threats. Then as a fourth step, now we try to bring in awareness to the people. We train them, at all levels in the organization. That is a key for people to understand the importance of the digital lessons. And then, as a fifth step, we work as a backup plan. In terms of bringing in a very comprehensive and a wholistic distinct approach on people, process, as well as in technology, to see how the organization can withstand during a crisis time. And finally, we do a continuous governance of these data. Which is a key, right? It is not just a one-step process. We set up the environment, we do the initial analysis, and set up the strategy and continuously govern these data to ensure that they are not only not managed well, secure, as well as they also have to meet the compliance requirements of the organizations, right? That is where we help organizations to secure and meet the regulations of the organizations as per the privacy laws. So this is a constant process. It's not a one time effort, we do a constant process because every organization grows towards their digital journey, and they have to face all these as part of the evolving environment on digital journey. And that's where they should be kept ready in terms of recovering, rebounding and moving forward if things goes wrong. >> So, let's stick on that for a minute and then I want to bring Yusef into the conversation. So, you mentioned compliance and governance. When you're in digital business here as you say you're a data business, so that brings up issues, data sovereignty, there's governance, there's compliance, there's things like right to be forgotten, there's data privacy, so many things. These were often kind of afterthoughts for businesses that bolted on, if you will. I know a lot of executives are very much concerned that these are built in and it's not a one-shot deal. So, do you have solutions around compliance and governance? Can you deliver that as a service? Maybe you could talk about some of the specifics there. >> Sure, we offer multiple services to our customers on digital residents. And one of the key service is the data compliance as a service. Here, we help organizations to map the key data against the data compliance requirements. Some of the features includes in terms of the continuous discovery of data, right? Because organizations keep adding on data when they move more digital. And helping and understanding the actual data in terms of the resilience of data, it could be an heterogeneous data sources, It could be on data basis, or it could be even on the data lakes, or it could be even on on-prem or on the cloud environment. So, identifying the data across the various heterogeneous environment is a very key feature of our solution. Once we identify and classify these sensitive data, the data privacy regulations and the prevalent laws have to be mapped based on the business rules. So we define those rules and help map those data so that organizations know how critical their digital assets are. Then we work on a continuous monitoring of data for anomalies. Because that's one of the key features of the solution, which needs to be implemented on the day-to-day operational basis. So, we help in monitoring those anomalies of data, for data quality management on an ongoing basis. And finally, we also bring in the automated data governance where we can manage the sensitive data policies and their data relationships in terms of mapping and manage that business rules. And we drive limitations and also suggest appropriate actions to the customers to take on those specific data assets. >> Great, thank you. Yusef thanks for being patient. I want to bring in Io-Tahoe to the discussion and understand where your customers and Happiest Minds can leverage your data automation capability that you and I have talked about in the past. And I mean it'd be great if you had an example as well, but maybe you could pick it up from there. >> Sure, I mean at a high level as Suresh articulated really, Io-Tahoe delivers business agility. So that's by accelerating the times operationalized data, automating, putting in place controls, and also helping put in place digital resilience. I mean, if we stepped back a little bit in time, traditional resilience in relation to data, often meant manually making multiple copies of the same data. So you'd have a DBA, they would copy the data to various different places, and then business users would access it in those functional silos. And of course, what happened was you ended up with lots of different copies of the same data around the enterprise. Very inefficient, and of course ultimately increases your risk profile, your risk of a data breach, It's very hard to know where everything is. And I realized that expression you used David, the idea of the forced match to digital. So, with enterprises that are going on this forced match, what they're finding is, they don't have a single version of the truth. And almost nobody has an accurate view of where their critical data is. Then you have containers, and with containers that enables a big leap forward. So you can break applications down into microservices, updates are available via APIs, and so you don't have the same need to to build and to manage multiple copies of the data. So, you have an opportunity to just have a single version of a truth. Then your challenge is, how do you deal with these large legacy data states that Suresh has been referring to? Where you have to consolidate. And that's really where Io-Tahoe comes in. We massively accelerate that process of putting in a single version of truth into place. So by automatically discovering the data, discovering what's duplicate, what's redundant, that means you can consolidate it down to a single trusted version, much more quickly. We've seen many customers who've tried to do this manually and it's literally taken years using manual methods to cover even a small percentage of their IT estates. With Io-Tahoe you can do it really very quickly and you can have tangible results within weeks and months. And then you can apply controls to the data based on context. So, who's the user? What's the content? What's the use case? Things like data quality validations or access permissions, and then once you've done that, your applications and your enterprise are much more secure, much more resilient as a result. You've got to do these things whilst retaining agility though. So, coming full circle, this is where the partnership with Happiest Minds really comes in as well. You've got to be agile, you've got to have controls and you've got to drive towards the business outcomes. And it's doing those three things together, we really deliver for the customer. >> Thank you, Yusef. I mean you and I in previous episodes we've looked in detail at the business case you were just talking about the manual labor involved. We know that you can't scale, but also there's that compression of time to get to the next step in terms of ultimately getting to the outcome and we've to a number of customers in theCUBE and the conclusion is, it's really consistent that if you can accelerate the time to value, that's the key driver, reducing complexity, automating and getting to insights faster. That's where you see telephone numbers in terms of business impact. So my question is, where should customers start? I mean how can they take advantage of some of these opportunities that we've discussed today? >> Well, we've tried to make that easy for customers. So, with Io-Tahoe and Happiest Minds you can very quickly do what we call a data health check. And this is a two to three week process to really quickly start to understand and deliver value from your data. So, Io-Tahoe deploys into the customer environment, data doesn't go anywhere, we would look at a few data sources, and a sample of data and we can very rapidly demonstrate how data discovery, data cataloging and understanding duplicate data or redundant data can be done, using machine learning, and how those problems can be solved. And so what we tend to find is that we can very quickly as I said in a matter of a few weeks, show a customer how they can get to a more resilient outcome and then how they can scale that up, take it into production, and then really understand their data state better, and build resilience into the enterprise. >> Excellent, there you have it. We'll leave it right there guys. Great conversation. Thanks so much for coming into the program. Best of luck to you in the partnership, be well. >> Thank you David, Suresh. >> Thank you Yusef. >> And thank you for watching everybody. This is Dave Vellante for theCUBE and our ongoing series on Data Automation with Io-Tahoe. (soft upbeat music)

>> Announcer: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hello, everyone. Welcome to this CUBE conversation. I'm John Furrier, host of theCUBE. We're here in our Palo Alto studios in California for a CUBE conversation with Suresh Menon, who's the senior vice president and general manager of Informatica of the master data group. Suresh, great to see you. We couldn't see you in person. Three-time CUBE alumni at Informatica World, industry executive. We're remote. Great to see you. >> Good to see you, John. Great to be back. Wish this was in person, but I think this is fantastic. >> Well, one of the things that's clear in my interviews over the past four months, we've been doing our best to hit the road and we've got a quarantine crew here. We're doing our part telling the stories that matter. Data now more than ever, COVID-19 has shown that the companies that are prepared, that have done the work, for the digital transformation, you know, putting the cliche aside, is real and the benefits are definitely there. And you're seeing things like reaction time, war rooms are being put together, because business still needs to go on. This is the reality. And so companies are seeing some exposure and some opportunities, and so a lot of things are going on. So I want to get your reaction to that, because there are changes on how customers are evolving with data. You guys have been at the forefront of that, pioneering this horizontal data fabric, data 4.0, amidst talks about. What are you seeing from customers? How are they approaching this? Because at the end of the day, they got to come out of the pandemic with a growth strategy and they got to solve the problems they've got to do today and be in position. What are you seeing for changes? >> So one of the most important things that we started seeing, there are about three big trends that we began to see starting in about late March, and share some of the data points that we saw across the world, starting with Italy, which was in the news earlier this year with the pandemic. We saw that in one week, the stats were that online or digital sales increased by 81% in a single week. And it's obvious when you lock down a large population, commerce moves to, away from the brick and mortar kind of model to being completely online and digital. The other part of it that we started seeing is we had already started seeing a lot of our customers starting to struggle with supply chain issues. As borders started closing, opening, and then closing again, how do you maintain a resilient supply chain? And a resilient supply chain also means being able to be really agile in terms of trying to identify alternate supply sources, be able to quickly onboard new suppliers, maybe in different parts of the world that are not so affected. And then finally, the last piece that we saw were every single CFO, chief financial officer, people who ran finance organizations at all of these companies, for them, it is almost as if you're driving down the highway and you suddenly run into, enter this fog bank. The first reaction is to hit the brakes, of course, because you don't know what's (microphone cuts out) so every CFO around the world started saying, I need to be able to understand what my cash flow situation is. Where is it coming in from? Where is it going out of? How do I reconcile across the geographies, lines of business? Because everybody realized that without an adequate cash reserve, who knows how long this thing is going to carry on? We need to be able to survive. And then the fourth element that has always been important for our customers is all about customer engagement, getting the best possible customer experience. That's just being turned up to 11, the volume, because as organizations are saying, there's disruption happening now. There are new ways in which consumers are going out there and buying products and services, and these things might stick. There's also an opportunity for some of these organizations to go out and enter into markets, gain market share, that they were not able to do in the past. And then how do you come out of this, whenever it is, how do we come out of it? It's always by making sure you're retaining your customers and getting more of them. So the underpinnings across all of this, whether it's supplier data, whether it's getting the most accurate product information delivered to your online channels, whether it is being able to understand your supply chain holistically with our data platform under it, and then finally customer experience depends on understanding everything end to end, including everything you need to know about your customer. So data continues to become top of mind for all of these organizations. >> You know, Suresh, we've had conversations over the past three years, and I can remember them vividly all about, and we've been really geeking out, but also getting very industry focused around, oh, the enablement of data and doing all these things, horizontal scalability, application enablement, AI CLAIRE, all these things are very relevant. But now with COVID-19, that that future's been pulled to the present. It's accelerated so fast that everything's impacted the business model. You mentioned supply chain and cash flow. The business is right there visible, and all these things are exposed and heightens the volume, as you said, and so everyone's seeing it happen. They can see the consequences, right? So this is like the most reality view of all time in any kind of is digital transformation, will it happen? So I want to get your thoughts on this, because I've been riffing on this idea of the future of work, the word work, workplaces, workforce, workloads, and workflows, right? So they all have work in them, right? We talk about workflows and workloads. That's a cloud term and a tech term. Workplace is the physical place, now home. Workforce are people, their emotional stability, their engagement. These things are all now exposed and all this new data's coming in. Now the executives have to make these decisions. This has really been a forcing function. So first, I'm sure you agree with all that, but what's your reaction to that? Because this brings up challenges that customers are facing. What's your thoughts on this massive reality? >> Yeah, I mean, this is where I think the other domain that is very important, which is most important for organizations if you have to be successful is really that employee or workforce understanding. We talk about customer 360s. We have to talk about employee 360s, right? And tie that to locations. And there are very few enlightened organizations, I would say, maybe three, four, five years ago, who had said, we really do need to understand everything about employees, where they work from, what are the different locations they go to, whether it's home and whether it's the multiple office locations that the organization might have. And it started quite realistically in the healthcare organization. There's a large healthcare provider here in California who many, many years ago decided that they want to create an employee 360, and considering it's doctors, it's nurses, it's hospital technicians and so on, who move from one hospital to another different outpatient clinics. And we are in a disaster-prone state, and what they said is I need to build this data foundation about my employees to understand where someone is at any given point in time and be able to place them so that if there is, let's say, an earthquake in one part of the state, I want to know who's affected, and more importantly, who's not affected who can go out and help. And we started seeing that mindset now go across every single organization, organizations that said, hey, I was not able to keep track, when the lockdowns were started, I was not able to keep track of which one of my employees were in the air at that time, crossing borders, stuck in different parts of the world. So as much as we talk about product, customer financial data, supplier data, employee data, and an employee 360, and now with a lot of state and local governments creating citizens 360s has also now become top of mind because being able to pull all of this data together, and it's not just your traditional structured data. We're also talking about all the data that you're getting, the interaction data from folks carrying their phones, mobile devices, the swipes that people are doing in and out of locations, being able to capture all of that, tie it all together. Again, we talk about an explosion in volume, which I think is to your point, bringing in more automation with CLAIRE, with artificial intelligence, machine learning techniques, is really the only way to get ahead of this, because it's not humanly possible to say, as your data scales, we need to get the same linearly, the same number of people. That's not going to happen. So technology, AI, has to solve it. >> Well, I want to get to AI in a second. It's on my list to ask you about CLAIRE, get the update there. But you mentioned 360 view of business and the employee angle's definitely relevant. Talk more about this 360 business approach, how are customers approaching it across the enterprise. Certainly now more than ever, it's critical. >> Right, so the 360s have always been around, John, and I think we've had these conversations about 360s now, for the last few years now, and a lot of organizations have gone out and said, create a 360 around a particular, whichever one specific business-critical domain that they want to create a 360 out of. So typically for most organizations, you're buying parts, raw materials from a supplier. So create a supplier 360. You really need to understand is there risk there in the supply chain? Am I allowed to do business with a lot of these suppliers? It's data that helps them create that supplier 360. The product is always important, whether you're manufacturing your own, or if you're a retailer, you're buying these from your suppliers and then selling them via your different channels. And then finally, the third one was always customers, without which none of those organizations would be in business. So customer 360 was always top of mind. But, and there are ancillary domains, whether it's that's the employee 360 we just talked about, finance 360, which are of interest maybe to specific lines of business. These are all being done in silos. If you think about creating a full 360 profile of your suppliers, of your products, of your customers, the industry has been doing it now for a few years, but where this pandemic has really taught a lot of organizations is now it's important to use that platform to start connect (microphone cuts out) a line all the way from your customers via their experience all the way back to your suppliers and all the different functions and domains and 360s that it needs to touch. And the most, I guess real-world example a lot of us had to deal with was the shortages in the grocery stores, right? And that ties all the way back to the supply chain. And you're not providing your best possible customer experience if the goods and products and services that customers want to buy from you are not available. That's when organizations started realizing, we need to start connecting the customer profiles, their preferences, to the products, our inventory, all the way back down to suppliers, and are, for example, can we turn up the production in a particular factory, but maybe that location is under one of the most stringent lockdown conditions and we're not able to bring in or increase capacity there. So how do you get a full 360 across your entire business starting with customer all the way back to supplier. That is what we are saying, the end-to-end 360 view of a business, or as we, there's too many words, we just call it business 360. >> Yeah, it's interesting, and I'm interviewing a lot of your customers lately and talking some of the situations around COVID. There's the pre-COVID, before COVID, during COVID, now looking after COVID. Some have been very happy and well-prepared because they have been using, say, Informatica, and had done the work and are taking advantage of those benefits. I've talked to other practitioners who are struggling with trying to figure out how to architect, because what your customers who've been successful have been telling me is that, look at, we're in good shape right now because we did the work prior to COVID, and now they are being forced to have a 360 view not because it's a holistic corporate mission. It's they have to, right? People are at home, so it's not like, hey, let's get a 360 view of the business and do an assessment and do better and enable things. No, no, no. There's business pressure. So they're enabled. Now new types of data's coming in. So again, back to the catalog and back to some of the things that you guys have been working on. How do you talk to your customers now that they're in COVID for the ones that have been set up before COVID and the ones now that are coming to the table saying, okay, I need to now get quickly deployed with Informatica while I'm in, during the state of COVID so I can have a growth strategy coming out of it, so I don't make these mistakes again. What's your thoughts? >> Absolutely, and I think that the, whether an organization has already, a customer has already laid the groundwork, has the foundation before COVID, and the ones who are now moving full steam ahead because they're missing capabilities in those functions. The conversation is in reality more or less the same, because even for those who have the foundation, what they're starting to see is new forms of data coming in, new forms of, new requirements being placed on the, by the business on that infrastructure, the data infrastructure, and being able to, most importantly, react very, very quickly. And even for those who are starting off right now from scratch, it's the same thing. It's need to get up and running, need to get the answers to these questions, need to get the, we need to get the problems to these solutions as soon as possible. And that the theme, or I guess the talking points for both of those customers is really two things. One is you need agility. You need to be able to bring these solutions up to life and delivering as soon as possible, which means that the capabilities, the solutions you need, whether it's bringing the catalog, understanding where your data is very, very quickly, your business critical information. How do you bring that in, all of that data, and integrate that data into a 360 solution, be able to make sure it's of the highest quality, enrich it, master it, create those 360 profiles by joining it to all of this interaction, transaction data. All that has to be done with the power of technologies like CLAIRE, with artificial intelligence, so that you are up and running in a matter of days or weeks, as opposed to months and years, because you don't have that time. And then the other one which is quite important is cloud, because all of this capability needs infrastructure, hardware to run on. And we've started seeing a lot of, let's say cloud-hesitant verticals, entire verticals now in the last two to three months suddenly going from yeah, cloud is maybe somewhere down the road, as far as our future's concerned. But to now saying, we understand that we have to go to a cloud when our technicians are not able to get access to our data centers to add new machinery in there to take care of the new demands, that migration to cloud. So it's that agility and cloud which really is the common theme when we talk to customers, both- >> Yeah, and now more than ever, they need it, 'cause it's an important time, and it's going to be an inflection point, for sure. There'll be winners and losers, and people want to be on the right side of history here. Suresh, I got to ask you about AI. Obviously CLAIRE's been a big part of it. Now more than ever, if you have bad data, AI can be bad too. So understanding the relationship between data and AI is super important. This is going to be critical to help people move faster and deal with more data as soon as they're dealing with now. What's your thoughts on the role AI will play? >> Oh, AI has a huge role to play. It's already begun to play a huge role in our solutions, whether we start from catalog to integration to 360 solutions. The first thing that AI can really do very, very well is, we've gone from folks who said, let's take supply chain. There were maybe three sources of supplier data that used to come into creating a supplier 360. Today, there are hundreds of sources. If you go all the way to the customer 360, and we are talking about 1,300, 1,400 different sources of data with 90% of them sitting up in the cloud. How is it humanly possible to bring all of that data together? First of all, understand where customer information is sitting across all of those different places, whether it's your clickstream data, call log data, whether it's the actual interaction data that customers are having with in-store, online, collecting all of that information, and from your traditional systems like CRM, ERP, and billing, and all of that, bringing all that together for understanding where it is, catalog gives you that Google for the enterprise view, right? It tells you where all this data is. But then once you've got that there, it also tells you what its relative quality is, what needs to be done to it, how usable is it. To your point of if it's bad data, at least what AI can do first of all is tell you that these are unreliable attributes, these are ones that can be enriched. And then, and this is where AI now moves to the next level, which is to start inferring what kind of rules that are in our, let's say, repository across integration, quality, and mastering, and bring, and matching, bring all that together and say, here, you as the developer who's been tasked with making this happen in a matter of days, we are going to infer for you what you need to do with this data, and then we will be able to go in and bring all these sources in, connect it, load it up into a 360 solution, and create those 360 profiles that everybody downstream, whether it's your engagement systems and other. So it's really about that discovery, that automation, as well as the ability to refine and suggest new rules in order to make your data better and better as you go along. I think that's really the power of CLAIRE and AI. >> I love the Google for the enterprise or data, because the metaphor really is about finding what you're looking for. It's the discovery piece, as you said, to make it easy, and Google did make it easy to find things, which is what their search engine did. But if you look at what Google did after that, they had to have large scales. SREs is what they call them, site reliability engineers, one engineer for thousands and thousands of servers, which they, revolutionizing IT and cloud. You guys are kind of thinking the same way, data scale, right? So it's Google in terms of discovery, right? Find what you're looking for, catalog, get it in, and get it out quest, make it available for applications. But you're kind of teasing out this other point where the AI comes in. That's scale. >> Yes. >> That's super important nuance. >> Absolutely. >> But it's key to the future. >> Absolutely, because when we are starting to talk about now not just tens of millions of records when it comes to customer data or product experience dat and so on. We are already talking about organizations like Dell, for example, with our customer 360, with billions of records going in, which would be equivalent to the scale of, if you look at Google search engine business back maybe 10, 12 years ago. So yes, we are talking about within the context of a single organization or a single company, we're already talking about volumes that were unthinkable even five years ago. So being able to manage that scale, be able to have architectures, technologies that are able to autoscale, and the advantage of course is now we've got an architectural platform that has microservices. As loads start increasing, be able to spawned new instances of those microservices seamlessly. Again, this is another part where AI comes in, AI being able to say, in the old days it was somebody had to see that the CPUs are overloaded to about 100% before someone realized that we have to go out and do something about it. In this new world with AI managing the ops layer, being able to look at is this customer bringing in another, in the cloud rack, cloud world, in a SaaS world, bringing in a billion records that they want to push through in the next 10 minutes, be able to anticipate that, spawn the new infrastructure and the microservices, and be able to take care of that load and then dial those back down when the work is done. This again, from an ops perspective as well, from, so we are able to scale instead of sort of having, let's say, 1000 SREs, I think, to your example, John, have only 10 SREs to make sure that every, look at the dashboard and make sure everything is going well. >> Well, I've been covering you guys for a long time. You guys know that. And I'm a big fan. I always had been a fan of the vision that's playing out. Large scale data, large scale discovery, fast and easy, integrating that into applications for business value. It's not just the data warehouse and just park something over here. This is a mindset. It's a foundational enablement model. You guys have done an amazing job. And now more than ever, it's I think more understood because of the pandemic. >> Absolutely, and people are making that direct connection between the business outcome and the value of having this data foundation that does all the things we described. >> Suresh, great to see you, and bummer we couldn't be in person, but hey, the pandemic hit. Informatica World when virtual. A lot of different events. I know you guys have a lot of things going on virtually, and you're engaging well. Everyone's working at home. Not a problem. Most of the techies can work at home. It's not a big deal. But you've got remote customers. You guys are engaging with them. And congratulations and great to see you. >> Same here. Thank you so much. >> All right, Suresh Menon. He is senior vice president, general manager of master data at Informatica. Data's more important than ever. We're seeing it, this is a foundational thing. If it's not enabling value, then it's not going to be a good solution. This is the new criteria. This is where action matters. People who need data and need to integrate into new workloads, new applications across workforces and new workplaces. This is the reality of the future. I'm John Furrier with theCUBE. Thanks for watching. (bright music)

>> live from Las Vegas. It's the queue covering Inform Attica, World 2019. Brought to you by in from Attica. >> Welcome back, everyone to the cubes. Live coverage of infra Matic A world. I am your host, Rebecca Night, along with my co host, John Furrier. We are joined by sir Rushman, and he is the senior vice president and general manager. Master Data Management here it in from Attica. Thank you so much for coming on the show. >> Thank you. It's great to be back. >> Great to welcome a Cube alum. So a major theme of this conference is customer 3 60 It's about customers need for trusted accurate data as they embark on their own digital transformation initiatives. Can you just talk a little bit about what you're hearing, what you're hearing from customers, what their priorities are? >> Yeah, absolutely. You know, with MGM, the promise of MGM has always been creating a trusted, authoritative version ofthe any business critical entity on DH who are the most important business critical entities for any organization customers. So almost 80 to 90% off. You know, if our customers are talking about re inventing a new customer experience because some >> of the >> things that they've been telling us is that we've all learned, you know, in the past that bad customer experience means that, you know, we've all had those experiences. We goto hotel, we use a particular airline, we have bad experience and we say, Promise ourselves we'll never go back there again. So organizations have always for years now understood that there is a cost to not delivering a good enough customer experience. The big change that I'm hearing, at least over the last you know, you're also now and especially at this event, is that organizations have now been able to quantify what great customer experience can mean in terms ofthe a premium that they can charge for that products or services. Now that is a big shift. When you start thinking about saying if I'd deliver a better customer experience, I'm actually be able to charge 10 cents more for a cup of coffee. I can charge, you know, 20% more for an airline ticket that now has a direct impact on the top line >> and data drives. This obviously data's a key part of it. What's changed this last year, I mean a lot happened. We see on the regular tourist my one year anniversary of GDP are a lot of pressure around regulation. We see everyone sees Facebook and goes, Oh my God, maybe I don't want to follow that trap. Woman Enterprise pressure to develop sass like applications with data because we know what cloud native and born the Cloud looks like. We've seen companies come out of the woodwork from his fresh start and used data as part of the input with a IE application for great software. So now the enterprise I want to do that exactly. It's hard, >> it's hard. And I think you know, they're in a lot of organizations minds, you know, collective minds. This is cushion pulled because in order to deliver that best possible customer experience, they realize they need to gather more data about us, right? Every in every touch, point, every interaction. If you can gain that complete 3 60 view, it just means that you'd be able to deliver better possible experience. But now you're gathering more data about customers into your example about Facebook. Now means that we in our custodians off what was you know, an explosion of data than what we used to have before. And if you're moving those to the cloud, how do I make sure that I don't end up, you know, in the front page of The Wall Street Journal? You know, like some of the other organizations have. So there is great, you know, volumes of data being collected. But how do I manage it? Secure it government effectively so that we don't have those? >> Don't ask a question. I have been talking a lot about fake news and Facebook lately because, you know, we're digital Cuba's official distribution. 10 years been doing it, putting out good payload with content. Great gets like yourself. But this really kind of too things. That's where I want to get your reaction to. There's the content payload. And then there's the infrastructure dynamics of network effect. So Facebook is an example where there was no regulation, I'll say they were incentive to actually get more data from the users, but she got content or data and then you got infrastructure kind of like dynamics. You guys are looking at an end to end. You got on premises to cloud that's it structure, and that's going to be powering the aye Aye, And the SAS data becomes the payload, right? So what? You're a zoo, a product management executive and someone thinking about the customer and talking to customers. How do you view that? What's the customers formula for success to take advantage of the best use of the content or data and digital while maximizing the opportunities around these new kinds of infrastructure scale and technology? >> Yeah, I think you know, they've come to the realization that data is not entirely sitting on premise animal, you know, in the in the in the old World, to get customer data, you go 23 applications of CR m nd R B and some kind of, you know, a couple of homegrown applications in on premise now for the same functionality. But that's wise of customer customer experience applications that whatever you call it, there's an app for it. And it happened to reside in the clouds. So now you have about 1,100 on average cloud applications that store components. So where do you where do you start bringing all of that content together? A lot of organizations have realized that, you know, do it in the cloud for two reasons because that's where the bulk of this data is being generated. That's where the bulk of this data is being consumed. But the other aspect of it is we're not no longer talking about hundreds of millions of records, but I just thought bringing in transaction data interaction later don't know billions of records, And where else can you scale with that? Much is other than the club s O. But at the same time, that is, there is a hybrid that is extremely important because those applications are sitting on premise are not going away. You know, they still serve up a lot of valuable customer data and continue to be frontline operation systems for a lot of the user. So a truly hybrid approach is being developed. I think that thought process is coming around where some domains live in the clouds. Some domains live on premise, but it's seamless experience across book. >> That's great insight I wanted Then follow up and ask you Okay, how did in from Attica fitted that because you guys want to provide that kind of horrors? Office scaleable data layer, depending on where the customer's needs are at any given time you got a pea Eye's out. There's things that Where do you guys How do you make that a reality? That statement you just made? >> Yeah. And the reality is eyes already being, you know, being lived today with a few of the few of our customers on it is that data layer that says, you know, we can, you know, bring data run work loads that are behind the firewall. We can do the same work, load in the cloud if that's where you want to scale the new workloads, but at the same time have a data layer that looks like one seamless bridge between the cloud and on premise. And that a number of different experiences that can, you know, help that we've invested in cloud, you know, designing and monitoring capabilities that allow view for a completely cloud like experience. But all of the data still decides on premise. It's still being managed and behind your firewalls, which is where a lot of the organizations are going as well, especially more conservative, more regulated organisations. >> One of things. I want to get your reaction to a swell, great great commentary, By the way, Great Insight is some success examples that might not be directly the inn from Attica, but kind of point to some of the patterns. Let's take slack, for instance, Great software. It's basically an IRC measures chat room with on the Web with great user experience. But the adoption really kicked in when they built integration points into other systems. So this seems to be a fundamental piece of informatics. Opportunity is, you kind of do this layer, but also integrating it. Because although you might have monitoring, I might want to use a better monitoring system. So So you're now thinking about immigration. How do you respond to that? What are you guys doing? Respected. Integration? What's What's the product touchpoints can He shared a commentary >> on Yeah, So you know, the openness off our entire data architecture and all of the solutions is something that we you know, I think they use the word Switzerland quite often. But what it also means is that you know, you are able to plug in a best of breed execution engine for a particular workload on a particular platform if you so desire. If you want to plug in a you know I am a model that happened to be developed on a specific let's say, an azure or a W You'd be ableto bring that in because the architecture's open completely FBI driven as a zoo mentioned. So we're able tto. Our customers have the flexibility to plug in, and we try to make that a little easier for them also, you know, as you might have seen some of the demos yesterday, we are providing recommendations and saying, You know, for this particular segment of your work, Lord, here are the choices that we recommend to you. And that's where Claire Gia, you know, comes in because it's very hard for users to keep up with all of the different possibilities. You know, our options that they might be having in that particular day, the landscape, and we can provide those recommendations to them. >> I want to ask about something you were saying earlier, and this is the company's heir using data to realize that they can charge a premium for a better customer experience. And that really requires a change in mindset from a gut driven decision making to a data driven decision making method and approach. How how are you seeing this? This mindset shift is it? Our company is still having a hard time sort of giving up my guts, telling me to do this in particular, with relationship to the new thie acquisition you made in February of all site. >> Yes. You know, I think the good news is, you know, across the board line of business leaders, CEOs, even boards are now recognizing custom experience. Customer engagement happened to be top of mind, but there's also equally react. You know, a recognition that data is what is going to help, you know, make this a reality. But so that was one of the reasons why you went out and, you know, do this acquisitions also, because if you think about it, customer data is no longer just a handful of slowly changing attributes like a name and address and telephone number or social media handles that, you know, you could be used to contact us. But it's really about now. Thousands of interactions we might have on the websites Click stream data Web chat, you know, even calls into call centers. All of this and even what we're tweeting about a product or service online is all the interactions and touch points that need to be pulled in and the dogs have to be connected in order. Bill that customer profile. So we have to do the scale, and that's something that Alcide, you know, has been doing very well. But it's now become more about just connecting the dots. So we can say, Here is this customer and this is the all the different Touchpoints customers had all the different products of purchase from us over the last few months. Few years. But now can we derive some inside some intelligence? So if I'm connecting four pieces of information cannot in for a life event, can I detect that an insurance customers ready to retire? Can I detect that this family is actually shopping for a vacation to Hawaii? That's the first level off Dr Intelligence Insight that we can now offer with. Also, the next level is also about saying >> cannot be >> understanding. You know, some of these, you know, intent. Can we also understand how happy is this customer, you know, have been mentioning competitive product, which can allow us to infer that person probably going to go off and buy a competitors product. If this problem they're having with this device or product is not resolved, so turn scoring, sentiment scoring. And now the third level on top of that which I think is really the game changer, is now. Can we in for what the next best action or interaction should be based upon all these things? Can we even do things such as, as I left here, not too happy customer with a particular maybe laptop that I, you know, perches I called the call center can before as a call is coming through, can we in for what I'm calling about based upon all of the interactions have had over the recent past and direct that call to 11 to 11 3 Technician who specialized in the laptop model >> that I have >> in orderto make me continue to be a customer for life. >> One of the biggest challenge is happening in the in the technology industry is the skills gap. I want to hear your thoughts on it and also how they help my how concerned are you about finding qualified candidates for your roles? >> So, you know, I think being a globally, you know, global organization with R and D centers distributed around the world. I think one of the luxuries we have is we're able to look across not just, you know, way from Silicon Valley, you know? And you know, there is a definitely a huge competition for skills over there. I think one of the things that we've been able to do is locations like Toronto we were just talking about. That's where Alcide is based. Extremely cool technology that's come out, that that's, you know, really transforming organisations and their approach. The customers stood guard, doubling bangle or Chennai Hyderabad. So you know, we are tapping into centers that have lots of skilled, you know, folks on DH calling hedging our you know, our approach and looking at this globally. Yes, there's definitely going to be even more of a demand as a lot of technology changes go for these skills. But I think, you know, by spreading you know that skills and having complete developed R and D centers in each of those locations helps us mitigate the farm. >> What about kids in school, elementary school, high school, college or even people retraining? Is there a certain discipline? Stats, philosophy, ethics will you see data opportunities for folks that may or may not have been obvious or even in place. I mean, Berkeley just had their first graduating class of data science this year. I mean, that's that's so early. People wanna hone in. What's what do you see? Its success for people attaining certain certain skills. What do you recommend? >> So I think that is definitely a combination ofthe technical skills, whether it is the new a n M L applications. But I think that is also, you know, in the past, we would have said, Let's go on higher than someone who has done computer science You know, on is very deep in that topic. But look at the problems we're trying to solve with data on the application of the animal. They're all in service of a business outcome, some kind of a business on DH more, we find people who are able to bridge the gap between strong application off the newer technologies on a animal and also an understanding off the broader world. And the business, I think, is really the combination of skills is really what's going to be required to succeed. >> Excellent, great note to end on. Thank you so much, sir. Arrest for coming on the show. >> Thank you. Thanks. >> I'm Rebecca Knight for John Furrier. You are watching the Cube.

>> From Las Vegas, it's theCUBE, covering Open Systems, the future is crystal clear with security and SD-WAN. Brought to you by Open Systems. >> Welcome back to Las Vegas everybody. My name is Dave Vellante and you're watching theCUBE. The leader in live tech coverage. We're here at the Cosmopolitan Hotel in the Chandelier Bar. At the Open Systems networking event, two gardener events this week in Las Vegas. On the heels of last week's AWS reinvent. Suresh Manchella is here. Is the Director of Global Infrastructure at Hillenbrand. Suresh, welcome to theCUBE. Thanks for coming on. >> Thank you. >> So, tell me about Hillenbrand. What you guys do, and what your role is. >> So, Hillenbrand owns two different companies. One is the Batesville Casket Company, which has been around about 150 years or so. And then the other side of the business is Process Equipment Group, where we do industrial pumps, separations, and heavy machinery, and things in that nature. >> Okay and your role as Global and Infrastructure, so it touches on all infrastructure presumably secure. Why don't you describe the scope of a little bit. >> So, my role is I'm the Global Director of Infrastructure from a corporate stand point. I oversee everything, you know network storage systems, compute cloud initiatives, and what not. Including some of the outside security operations as well. For Hillenbrand Corporate across all the companies that we own. >> So you guys manufacture industrial equipment, which presumably supports a time's critical infrastructure, so security is vital. What are some of the big factors that are driving your business and how do they affect your technology strategy? >> From a business standpoint, Hillenbrand, I'm in that space a lot. We try to acquire a lot of companies within that space and as a result we have many companies that are coming in and out our portfolio. With any other manufacturing companies, we have the same challenges where how do we integrate them faster? How do we integrate them in a secure and safer way? But at the same time, also enabling our businesses to take on the next step and evolve from a traditional manufacturing company to doing the digital transformation and taking advantage of technology to have the competitive advantage in the market. >> So I got to ask you, so we do a lot of these events everyone talks about digital transformation. It's become kind of a buzzword, but when I talk to practitioners like yourself, there's actually substance there and it relates to, it means a lot of different things to a lot of different people, but what's behind your digital transformation? Is it instrumentation, is it better collection of data? Is it using that for competitive advantage? All of the above? How would you describe it? >> You said it. It's all of the above. We have a lot of data that we're collecting over the years. About our customers. How they use our products. And what are some of the maintenance cycles that are going through our larger equipment, things of that nature. We have all of that information. I think we need to start looking at that information, and say how can we enable the business to provide the intelligence it needs to be proactive to reach out to the customers and say these machinery might need maintenance very soon, or things of that nature. So we want to provide that value to the business. >> So as part of that, Suresh, the instrumenting that machinery? Or is the machinery already instrumented? Is it translating analog to digital and providing connectivity, what's behind that? >> Some of our machinery that have been out there have been there for, you know, many many decades and many, many years. It's not they're not already there when it comes to IoT and things to that nature. But we're trying to look at some of those opportunities out there and see how we can better support our products. >> So that's a largely road map stuff. Right now, you're tryna focus on making sure that the business is working. You're getting products to market fast and winning the competitive game. Let's talk about security a little bit. Obviously Open Systems is a security company, manage security infrastructure. What's happening in security? What are the big trends, the mega trends that you see, and how are they affecting the way in which you approach technology and applying that to business advantage? >> So as a customer and as a manufacturing company traditionally we used to look at a company as you have your four walls: data center, all of your key elements are inside it and as we're going through what's the cloud transformation and everybody's talking about that cloud buzzword. Those boundaries are getting shattered. Information is everywhere. It's no longer within those four boundaries. So we have to start thinking security a different way. We used to think that, put some firewalls, put some controls around these things and things could be saved. But it's no longer the case. Everything is in the cloud. As a software as a service or platform as a service, infrastructure as a service. And they're all over the place. For the most part, you don't have access to those backing systems. So how do you protect them? We need to fundamentally change how we look at security and how do we protect it. Rather than focusing on the central systems, we have to focus on the endpoints at this point. >> So, different mindset for sure. Different sort of technology approach? Or similar practices with just different methodologies? How do you describe that? >> It's certainly a different methodology. The focus is certainly shifting. It's no longer centralized. It's decentralized. It's information everywhere. Information overload. It could be on your phones. It could be on your desktops. It could be on your laptops. It could be on servers in the cloud. Cloud service providers, there are a lot of things that come into play, when you're talking about the security the data that's scattered all over the place. >> So you're a customer of Open Systems, is that right? >> Yes we are. >> Maybe you could describe what you do with them and what your relationship has been with them? How do you apply their technique? >> Hillenbrand owns a company based out of Germany. And they've been a long standing customer of Open Systems for many, many years. So as a part of the acquisition, we got to know Open Systems and the value that their adding to in the SD-WAN spaces, and security space. Which is quite phenomenal. >> Okay, so you're part of the role as it relates to Open Systems is through that other division of the company, so how you apply their tech? What are you doin' with it? >> We utilize Open Systems as our SD-WAN provider outside the U.S. Primarily that division that we had was outside the U.S. for the most part. As we are getting to know more and more about Open Systems, over the years, it's a no brainer for us. They can provide a very reliable service that's scalable, very quick turnarounds. And that's certainly fitting in well with our MNA strategy where we acquire a company and try to integrate these things we cannot wait several months for and ambulance provider to drop a circuit and get them in, and things to that nature so with Open Systems, the SD-WAN concept, you only need an internet connection and they do all the magic behind the scenes and put it all together. >> So it's cloud like in the sense that it's sort of a managed service. But it's not necessarily remote cloud services, it could be on prim. >> Yes, it can be anywhere. >> Eventually at the edge. >> Yeah. >> So it fits into the roadmap. What's the biggest security challenge that you face as a practitioner today? >> The biggest security challenge that we have is protecting the data that's everywhere. The biggest challenge is knowing where the data is today. If anybody can solve that problem, I'd like to know. The first one to know that. It's quite a challenge for everybody lately. >> It's an arm's race, isn't it? >> It Is. >> Good. Well, Suresh, thanks very much for coming to theCUBE. It's a pleasure meeting you. >> Thank you. >> Keep it right there everybody, we'll be back. From Las Vegas at the Open Systems networking event. You're watching theCUBE. (upbeat music)

>> Announcer: Live from Las Vegas, it's theCUBE! Covering Informatica World 2018. Brought to you by Informatica. >> Welcome back everyone. This is theCUBE's exclusive coverage of Informatica World 2018. Live here in Las Vegas at the Venetian Hotel. I'm John Furrier, co-host with Peter Burris. Here for the next two days of wall to wall coverage. Our next guest is is Suresh Menon, Senior Vice President and General Manager of the Master Data Management group within Informatica. He's got the keys to the kingdom, literally. Welcome back, good to see you. >> Thank you for having me. >> The key of all this pun intended is the data. And the cataloging's looking good. There's a lot of buzz around cataloging. What you guys have as a core product. Your customers love the product. The world's changing. Where are we, what's the update? >> Catalog is extremely important. Not just to enterprise data, the entire landscape by itself. But it's equally very exciting for MDM. Cause what has the potential to to is transform the way in how quickly people can get value out of MDM. Cause a combination of metadata and artificial intelligence through machine learning is what can create self-configuring, self-operating, even self maintaining Master Data Management. And that's extremely important because in today's world, the digital world that we live in, the explosion of data. The explosion of data sources. The new kinds of data that MDM is being asked to master, correlate and link with is becoming so huge that it's not humanly going to be possible to manage/curate this data. And you need to have AINML, and the underlying metadata awareness that the catalog brings, in order to solve these new problems. >> So Suresh, after you came onto theCUBE last year. You left and I said, there's a question I should've asked him. I'm going to put you on the spot. If you could do it. If you could create a new term for this Master Data Management. And where it's going. What would you call it? >> Yeah. You know Master Data Management has been around not for very long. About eight or nine years. It doesn't begin to describe the kind of problem that we're trying to solve here today. The only one that I can think of is 360's. It's more about getting the complete holistic view of all the business critical entities that you as an organization need to know. And 360 has traditionally been used around customer. But it's not only about the customer. You need to understand what products the customer owns. Engineer a 360 around their product. You need to understand how those customers interact with employees. You need an employee 360. You need an asset 360. How can you even begin to do householding, if you don't do a location 360? >> I want to build on that. In many respects it's the ability to sustain the context of data for different personas, for different applications, for different utilizations. So in many respects, Master Data Management really is the contextual framework by which an organization consumes data. Have I got that right? >> Absolutely. It is the you know. Another way to describe that would be it is what delivers the consistent authoritative description where you have the semantics being completely differently described in all of these cloud applications. We've gone very far away from the days maybe ten years ago, where you had a handful of CRM and ERP applications that you needed to disambiguate this information. Today I think I was reading this morning that an organization on average has 1,050 different cloud applications. And 3/4 of them are not connected to anything. And the describing, creating, authoring information around all these business critical entities. MDM is becoming the center of this ultra-connected universe in another way that I would look at it. >> It's also a key part of making data addressable. And we talked about this last year. But something that I have observed that's been happening since last year. The storage vendors have been radically changing their view. They're going to be have storage, but their data layer is sitting in all the clouds. That's interesting. That means that they're seeing that there's a data abstraction kind of underneath Informatica if you will. If that happens then you have to be working across all the clouds. Are customers seeing that? Are they coming to you saying that? Or are you guys getting out front? How do you view that dynamic? >> Customers are seeing that, have been seeing that for the last two to three years. As they have started taking these monolithic, very comprehensive, on premise applications to a fragmented set of applications in the cloud. Where do they keep a layer where they have all this business critical data in one place? And they're beginning to realize that as they move these things to the cloud, these applications are moving to the cloud, it's going from one to a couple of hundred. Master data is being seen as that layer that basically connects all these pieces of information together. And very importantly for a lot of these organizations, data that's proprietary to them. That they don't necessarily want locked up in an application that may or may not be there a couple of years down the road. >> The value shifting from state commodity. Even I was talking last week with the guys from NetApp about a great solid state drive they're going to have. But that values up top where the data is. And they have the data stored. So why not facilitate? And you guys can take it and integrate it into the applications, into the workloads. How is that going with respect to say catalog or the edge, for instance? How should a customer think about MDM? If they have to architect it out, what's the playbook? >> The number one thing is where the catalog comes in is first of all trying to identify in this highly fragmented universe you now have. As to where all your fragments, or master data reside. This is where the catalog comes in. It gives you in one Google-like text search, tells you where all the customer master attributes are residing across the landscape. Third party, on premise, in the cloud. The catalog will also tell you what the relative quality is of those those attributes. And then by apply AINML to it, be able to now figure out how those pieces of data can be transformed, cleansed, enriched and brought into MDM. The catalog has a role to play within MDM. What are the most appropriate matching and linking rules? What are the most appropriate survivorship trust tools that you need to apply? And how do you secure all that data that's now sitting in MDM? Because it's now in the cloud, and you know data security and protection is top of mind for most-- >> Talk about AI over at MDM. Because last year Claire was announced. We've seen certainly with GDPR that AI will play a role. Machine learning and AI. It's all coming together. The relationship between MDM and AI. Natural to me, seems like it's natural. How do you guys see the fit between AI and MDM? >> It is fundamental to MDM. And where we've begun our investment in AINML is one of the most core capabilities around MDM, which is being able to recognize potential duplicates. Or detect non-obvious relationships across this vast set of master data that's coming in. We've applied AINML, and we'll see a demo of that tomorrow, and we'll here in Vegas, is using machine learning on top of the world's best matching algorithms, in order to infer what are the most appropriate strategies in order to link and discover these entities? And build a relationship graph, without a human having to introspect the data. >> One of our predictions is that over the course of the next few years companies are actually going to start thinking about networks of data. That data is going to get the network formation treatment. That devices, and pages, and identities and services that we've gotten in the past. It does seem as though MDM could play a very, very important role in as you said identifying patterns in the data, utilization of the data. What constitutes a data node? What constitutes an edge? Number of different ways of thinking about it. Is that the direction that you see? First of all, do you agree with that notion of networks of data? And is that the direction you see MDM playing in the future? >> Absolutely. Because up until now MDM was used to solve the problem of creating a distinct node of data. Where we absolutely had to ensure that whatever it is then node was describing is actually the entire, complete, comprehensive entity. Now the next step, the new frontier for MDM is now about trying to understand the relationships across those nodes. And absolutely. MDM is both about that curation that governs, which is very important for GDPR and all of the other initiatives out there. But equally importantly now being able to understand how these entities are related across those, the graph of all of those nodes now. >> Weave in the role that security's going to play. Because MDM can... Well we'll step back. Everybody has historically figured that either data is secure or it's not. Largely because it was focused on a device. And if you have a device, and secure the device, all the data on that device got equally secured. Nowadays data is much more in flight. It's all over the place. It's a lot of different sources. The role that security plays in crafting the node, in privatizing data and turning it into an asset, is really important. But it could really use the information that's MDM to ensure that we are applying the appropriate levels of security, and types of security. Do you see an evolving role between MDM and data security? >> I would actually describe it differently. I would say that security is now the core design principal for MDM. It has to be baked into everything that we do around designing MDM for the future. Because like you said, we've again gone away from some handful of sources, bringing data into MDM in a highly protected, on premise environment with a very limited number of consumers. Now we have thousands of applications delivering that data to MDM. And you've got thousands of business users. Tens of thousands of them. Applications all leveraging that master data in the context of those interfaces. Security has never bee more important for MDM. This is again another way of security. And I want to bring catalog back again. Catalog is going to automatically tell the MDM configuration developer that these are pieces of data that should be protected. This is PII data. The the health data. This is credit data. That security is implicit in the design of those MDM initiatives. >> I think that's huge with cloud and connected edge in the network that is critical. I got to ask you. I now we're tight on time. I want to get one more question in. Define intelligent MDM. I've heard that term. What does that mean to you? You mentioned security design in the beginning. I get that, what that is. But I heard the term intelligent MDM. What is the definition of that? What does it mean? >> It really means MDM that is built for three new imperatives. One is being able to scale, what I would call digital scale. It's no longer enterprise scale. It is about being able to make sense of interactions and relationships, and being able to use the power of the catalog, and AINML, in order to connect all of these dots. Because connecting these dots is what's going to deliver immense business value to those organizations. Facilitate the rise of the business user, and their requirements. Intuitive interfaces that allow them to perform their day to day interaction with MDM. And finally time to value. Intelligent MDM should be up and running, not in months or years, but in weeks if not days. And this is where the power of catalog, power of machine learning, can make this a reality. >> That's a great clip. I'm going to clip that. That's awesome. And then putting it into action, that's the key to success. Suresh, thanks for coming on. Great to see you. >> Thank you very much. >> As always. You've got the keys to the kingdom, literally. MDM is at the center of it all, the things going on with data from cloud, edge computing, all connected. I'm John Furrier with Peter Burrs bringing all the action here at Informatica World 2018. We'll be back with more after this short break.

>> Annoucer: Live from Las Vegas, it's theCUBE covering Dell Technologies World, 2018. Brought to you be Dell EMC and it's ecosystem partners. >> Well, welcome back to Las Vegas theCUBE continuing our coverage here of Dell Technologies World 2018. I'm John Walls here on theCUBE, along with Keith Townsend, and we're joined by Suresh Sathyamurthy, who is the Vice President of Cloud and Infrastructure Solutions Marketing at Dell EMC. Suresh, thanks for joining us. >> Thank you for having me. >> You bet. All right, so we're about two days in. >> Okay. >> To the show here. How's it been going for you, and what are you hearing from customers? >> It's been fantastic. I've had a few customer meetings since I got here. The amazing thing is the interest in IT transformation and digital transformation. There used to be a time when we do these conferences, the conversation would be around products, right? Like what's new with the products, what's coming up for launch? Now they're talking about transforming their IT. How do they transform their data center with solutions and the products that span server, storage, data protection, cloud? It's amazing. I'm seeing that shift of the conversation going from products to transforming your data center. >> And what's accelerating that? Because to me, that's the kind of conversation or thought process that folks in that community should have been having for some time. So, what's the acceleration now? >> So we did third party study with ESG where we surveyed about a thousand executives to find out what is it that they were interested in, and what do they think about IT transformation, and why it matters to them. Here's what we found out, and these are just a few data points. The full study is available on our website. We found out that they believe that they're going to be three x faster, in terms of completion of their IT projects, twice as likely to meet and exceed their revenue goals, and they would have 33% more budget to invest in innovation. If you think about it, that is spectacular. That is amazing. There used to be a day and age when the only conversation around IT would be, how do you reduce cost. Now we are having a conversation about how do you create new business models. So IT has transformed from this backend function to something that is enabling the businesses to build new business models, create new revenue streams, manage customer experience better, and I think that is at the heart of why the conversations have shifted. >> So let's talk about some technologies. What are some of the resulting technologies or changes in technology? Anything emerging that you'd like to talk about? >> The two things that I think is happening now, the first is cloud, the acceptance of hybrid cloud and how the cloud is being leveraged for this transformation. And the second is the use of data through the technologies like artificial intelligence and machine learning. And if you think about those technologies, they aren't really something that has come out of nowhere. It is an extension of a data continuum that we have been having. So the way I look at it is, you have creation of data, you have analysis of data, you have machines that learn from data, and then using the data to act as the data continuum. We have always had creation of data. It came from traditional applications, now it's coming from cloud applications, as well as endpoints, and IOT. So it's increasing the volume of data that is coming in. That has changed how data has been ingested. So storage of data has shifted. It's no longer about scale-up architectures, it's about scale-out architectures and software-defined architectures. And then there are technologies like Hadoop and Splunk and SAP, for which we provide ready solutions for that's going to help you analyze the data. So a natural part of this extension is how do you get those machines to use the data to learn and improve themselves, and you train them to go do that. That's where machine learning comes in, and it's a critical part of what we want to provide infrastructure for. And the final piece of it is acting on it, which is where I see AI play, where you have application status substituting for human intelligence in making decisions and acting on that information. >> So talk to us about the real conversation. It's about making it real. We're at. >> Yes. >> Dell EMC World 2018, and the theme is making it real. Read a stat yesterday, survey, 50% of CIOs believe in the next few years they are going to have an AI project. >> Sarush: Yes. >> You know what? I asked Siri to play a song for me the other day. I asked her five times and ended up picking it up and just typing in the solution. AI is all over the place in definition. >> Sarush: Correct. >> As you're having conversations, what are the types of projects and the scope of projects that customers want to engage AI and machine learning to achieve what business outcome? >> Yes, so it actually depends by industry, but the way I think about it is now as companies look at their application infrastructure, typically large enterprises are probably about 5000 applications, right? And when the time comes to upgrade the software and upgrade those applications or write new applications for new customer experiences or new business models, they see AI as an integral part of the design point in building those applications. That has never been the case in the past, right? So you have now cloud native applications evolving, and I would bet that any cloud native applications that is either customer facing or is going to be critical to the decision making of an business or enterprise, is going to have AI built in by default. Now this would change by industry. So if I'm taking supply chain, for example, Jeff Clarke talked about this in his keynote on how Dell EMC is changing supply chain with using machine learning. The other one was customer service and support, where we have a product called Pro Assist, that uses predictive analytics. The amazing thing is we reduced our time-to-service the customer 91%. So imagine what AI can bring to those applications that have already existed that are now getting better, faster, and more intelligent in terms of servicing our customers. And the experience of our customers are going to change as well. Now it is not just what we provide to our customers in terms of platforms, we are customers of these technologies as well. So we talked about the PowerMax, which was launched this morning. It makes six billion decisions a day. It has built in machine learning, and it's helping the storage administrator's job be more easier because the decisions need not be made by humans anymore. It's optimizing by itself. It's amazing how much these softwares are going to evolve with technologies like AI. >> So I love the fact that six billion decisions are being made. >> Everyday. >> Everyday. I can't even decide what I'm having for dinner tonight. >> That's a very important decision though, just like that song you wanted to play on Siri. >> Exactly. >> 'Cause I want to ask you about it later. >> But what's really interesting, is where the control plane and processing and this activity will take place. Not just the PowerMax, but a lot of announcements today around FPGAs in servers, up to eight GPUs in a server. >> Sarush: Correct. >> Are customers prepared to now manage those environments or are they looking to have that stuff outsourced to a Google or a local Dell EMC partner to lend that expertise? Where is the expertise for all of this AI? >> We believe that the world is going to be multi-cloud. We have done third party research and surveys where we found that 81% of even our own customer base, are going to be multi-cloud. So our intent is to build technologies that is agnostic of where the data resides. You should be able to analyze your data in a public cloud environment, in a private cloud environment, or in hybrid cloud environment. And depending on what sort of security compliance requirements you need to meet, you make the choice, and we build the technologies for you. A part of what we also do, is rather that just provide a compute platform, yes, we did provide 840 and 940 XA, the PowerEdge servers with these eight GPUs that's going to help you analyze your data, but we also provide ready solutions for machine learning where compute, storage, networking and the software is packaged in. And you buy one box that you plug in to do the analysis as well. And you can also have these applications written on our cloud providers that we partner with to have those done as well. You could build your cloud native applications that use AI on top of the VMware and Pivotal and Dell EMC infrastructure, which Pat Gelsinger from Vmware talked about yesterday as well. So it should not matter which cloud the data resides in and where the analysis actually happens. We will be able to provide the infrastructure for you in private, public, or hybrid environments. >> Yeah, when you're talking about machine learning, and you think about all this rich data that's coming in and then processing it, making some analytical evaluation of it through AI, give me an example, if you would, of something that is capable in that chain of events today and just maybe 12 months ago, 18 months ago, wouldn't happen, couldn't happen. >> Oh, the example that I just used about PowerMax and the applied machine learning that we have built into the product. We did announce the PowerMax same time here, and I was here talking about one of the Vmax versions last year. It didn't have machine learning built into it. Today a storage array that can process decisions by itself, without the involvement of a storage administrator, make a decision on which media to optimize to get maximum performance off it. And we are just 12 months since last Dell EMC World. So it's a real time example that we have employed within our technologies to see how we can change those, and that's going to rapidly accelerate how much involvement humans need to have in these decisions as well. >> So that bring up an interesting point. Six billion decisions, those decisions can be made because the process is extremely close to the data. >> Sarush: Yeah. >> So super low latency between the two. You guys gave onstage today the example of, you know what you want your alternative vehicle to make the decision right there, and not send the decision up to the cloud. I have this theory that we've heard data has gravity, but now compute is starting to have that gravity. And there's this need that, this specialized eight GPUs, FPGAs, that equipment doesn't exist everywhere, but the data needs to get there. What are the conversations you're having with customers about data accessibility including the data where the compute is at? >> Yeah, it depends by industry, but the way we look at it, what we are hearing from our customers is to think about their edges as the core. It used to be edge to core to cloud. Now you have an intelligent edge and a distributed core. That is how it has changed over the last two, three years. Intelligent edge because your edges, the devices, the endpoints, the edges are making decisions themselves without having transferred data to the cloud, just like your autonomous car example. If you are waiting for data to come back from the cloud on whether you have to brake the car or not when there's an interference in front of you, that's not going to work, right? So the ability to have intelligent edges is becoming more essential. We keep hearing that from our customers, and we want to provide solutions that enable the edges to be smart. And we do. With rugged and fan-less embedded systems, as well as PowerEdge servers for the edges, as well. But that doesn't mean all the compute happens at the edge, the cloud is a critical part of where analysis happens. And if it is not real time streaming analytics where decisions have to be made at the endpoint, there is a lot of value in analyzing data that you've gathered over the years and using that data to learn from it and make decisions as well. A big chunk of that missing learning happens in the cloud. So I think it's a combination of both. It's not either or. We hear from our customers that they need intelligence in their edge, they need intelligence in their distributed core. And we will have solutions across both of those as well. >> So let's talk about some of the solutions at the edge. What's the fit and finish? I saw a huge, what is relative, in the data center. >> Sarush: Yeah. >> The new 840, not a big box. At the edge, that's a big box. Can't put that in my car. So what are some of the evolving technologies we'll see at the edge to handle this massive amount of data? >> So a big chunk of it is going to be, it has to be rugged because the edges can be, you have temperature variations from minus five degrees to 55 degrees. It has to be fan-less because it has to be optimal enough to fit into the size of any object or device that you want to do, and we offer solutions for those as well within our PowerEdge offerings. We have those as well. But what you would also see is we are, across our family of businesses with Vmware, we are also extending our software capabilities to the edge to gather that information and have compute on the edges as well. And in the core, like you said, we have these larger, more comprehensive PowerEdge servers to compute, to be able to process the data for machine learning as well. >> Man: Now how do I manage all of that? >> Ah, that's a great point. This is where our cloud strategy also comes in. The management of the components used to be on premise, application level, with applications that are for specific needs, right? You used to have storage resource management software where their specific design point was to manage your storage resources. That is changing now. So we have SAS offerings, like CloudIQ, which can manage your environment from anywhere, and it has spreadative analytics built into it as well. So your management is actually made easier because it creates a predictive health score that tells you how much involvement you need to have in go fixing the issue, and if you need to be woken up to go fix an issue, it's going to do that on your behalf. Right? So that's how it is changing as well. The management is increasingly becoming SAS based applications that have intelligence built into it and that connect across your data center. It not just manages your storage, it manages your network, it manages your compute. It knows what's happening in your infrastructure, and it's informing you on your behalf. >> Well with all this capability, can you just help Keith make a decision about dinner tonight? >> I'm thinking about waffles again. >> We're talking about one in six billion, certainly we can address that, can't we? >> We can, we can. And what I would suggest is you can pick any restaurant in Bellagio, right by the Bellagio fountains and you'll have fun. >> There you go. All right. >> Thank you. >> We appreciate the insights. Thank you very much for sharing your time with us tonight. >> Absolutely, thank you for having me. >> We'll be back with more. You're watching theCUBE live from Las Vegas at Dell Technologies World 2018. (upbeat music)

>> Narrator: Live from San Francisco, it's theCUBE, covering Informatica World 2017, brought to you by Informatica. (driving techno music) >> Hey, welcome back everyone. Live here in San Francisco, Informatica World 2017, this is theCUBE's exclusive coverage from SiliconANGLE Media. I'm John Furrier, host of theCUBE, with my co-host Peter Burris, head of research at SiliconANGLE Media, also General Manager of wikibon.com, doing all the cutting edge research on data, data value, what's it mean, cloud, etc. Check it out at wikibon.com. Next guest is Suresh Menon, who's the SVP and General Manager of Master Data Management Informatica. The key to success, the central brains. MDM, great, hot area. Suresh, thanks for coming on theCUBE. Appreciate it. >> Thank you for having me. >> So, MDM has been in almost all the conversations we've had, some overtly and some kind of implied through... Take a minute to describe what you're managing and what the role is in that data fabric, in that Data 3.0 vision, why Master Data Management is so important. >> Right, if you think about Master Data Management, there are two ways to look at it. The first one would be in terms of MDM, let's follow the definition. Master Data is really about all the business critical entities that any organization is, you know, should be concerned about. So if you think about customers and products, that's the two most critical ones, and that's really where Master Data Management began. But then you should also think about employees, locations and channels, suppliers, as all being the business critical entities that every organization should care about. Master Data Management is about making sure that you have the most trusted, authoritative and consistent data about these entities, which can then fuel the rest of your enterprise. MDM has been used in the past to fulfill certain specific business objectives or outcomes, such as improving customer centricity, making sure that you're onboarding suppliers with a minimal amount of risk, and also to make sure that your products as being described and syndicated out to the web are done in the most efficient manner. >> You guys have the Industry Perspective Monday night. What was the insight from the industry? I mean, how was the industry... I know Peter's got a perspective on this. He thinks there's opportunity, big time, to reposition kind of how this is thought, but what's the industry reaction to MDM? >> The industry reaction is renewed excitement in MDM. MDM started off about 10 years ago. A lot of early adopters were there. And as is usual with a lot of early adopters, there was a quick dip into the cycle of disillusionment. What you've seen over the last couple of years and the excitement from Monday is the resurgence about MDM, and looking at MDM as being a force of disruption for the digital transformation that most organizations are going through, and actually being at the center of that disruption. >> Well it's interesting, I almost liken this to... I'm not a physicist, I wish I was, perhaps... Physics encounters a problem, and then people look at this problem and they say "Oh my goodness, that's, how are we going to solve that?" And then somebody says "Oh, I remember a math technique that I can apply to solve this problem and it works beautifully." I see MDM almost in the same situation. Oh, we've got this enormous amount of data. It's coming from a lot of different sources. How do we reconcile those all those sources? Oh, what a... oh, wait a minute. We had this MDM thing a number of years ago. How about if we took that MDM and tried to apply it to this problem, would it work? And it seems to fit pretty nicely now. Do you agree with that? >> I agree with that. There's also a re-defninition of MDM. Because sometimes when you look at what people think about, "Oh, that was MDM from seven years ago. How does that apply to the problems I'm dealing with today, with IoT data, social network data, interaction data that I need to make sense of. Wasn't MDM for the structured world and how does it apply for the new world?" And this is really the third phase of MDM, going from batch analytics, fueling old real-time applications, whether it was marketing, customer service and so on. And now, providing the context that is necessary to connect dots across this billions and billions of data that is coming in, and being able to provide that insight and the outcome that organizations are hoping to achieve by bringing all this together. >> You mentioned... I just want to jump in for a second, cause you mentioned unstructured data and also the speed of data, getting the value. So data as a service, these trends are happening, right? The role of data isn't just, okay, unstructured, now deal with it. You've got to be ready for any data injection to an application being available. >> Suresh: Yes. >> I mean, that's a big fact too, isn't it? >> Absolutely, and organizations are looking at what used to be a batch process that could run overnight, to now saying "I'm getting this data in real time and I need to be able to act on it right now." This could be organizations saying, "I'm using MDM to connect all of this interaction data that's coming in, and being able to make the right offer to that customer before my competition can." Shortening that time between getting a signal to actually going out and making the most relevant offer, has become crucial. And it also applies to other things such as, you identify risk across any part of your organization, being able to act upon that in real time as opposed to find out later and pay the expense. >> I know this is not a perfect way of thinking about it, but perhaps it will be a nice metaphor for introducing what I'm going to say. I've always thought about MDM as the system of record for data. >> Suresh: Yes. >> Right? And as we think about digital business, and we think about going after new opportunities and new types of customers, new classes of products, we now have to think about how we're going to introduce and translate the concepts of design into data. So we can literally envision what that new system of record for data is going to look like. What will be the role of MDM as we start introducing more design principles into data? Here's where we are, here's where we need to be, here's how we're going to move, and MDM being part of that change process. Is that something you foresee for MDM? >> Absolutely, and also, the definition of... MDM in the past used to be considered as, let's take a small collection of slowly changing attributes, and that's what we master for through the course of time. Instead now, MDM is becoming in this digital age, as you're bringing in tens of thousands of attributes even about a customer and a supplier, MDM being part of that process that can grow, and at the same time, those small collection of attributes important as a kernel inside of this information, it's that kernel that provides the connection, the missing link, if you will, across all of these. And absolutely, it's a journey that MDM can fuel. >> We think that's crucially important. So for example, what we like to say is we can demarcate the industry. We think we're in the middle of a demarcation point, I guess I should say. Where for the first 50 years we had known process, unknown technology. Now we're looking at known technology generally speaking, but extremely unknown process. Let me explain what I mean by that. We used to have very stylized, as you said, structured data. Accounting is a stylized data form, slow moving changes etc. And that's what kind of MDM was originally built for, to capture that system of record for those things. Now we're talking about trying to create digital twins of real world things that behave inconsistently, that behave unpredictably, especially human beings. And now we're trying to capture more data about them, and bring them in to the system. Highly unstructured, highly uncertain, learning and training. So, help us connect this notion of machine learning, artificial intelligence back to MDM, and how do you see MDM evolving to be able to take this massive, new and uncertain types of data, but turn it into assets very quickly. >> Absolutely. It's a crucial part of what MDM is all about today and going forward into the future. It is the combination of both the metadata understanding about what it is that these data sets are going to be about, and then applying artificial intelligence through machine learning on top of it, so that... MDM was always about well-curated data. How can you curate data by human curation, how is that possible when you've got these real time transactions coming in at such high speed and such high volume? This is where artificial intelligence can detect those streams, be able to infer the relationships across these different streams, and then be able to allow for that kind of relationship exploration and persistence, which is key to all of this. Completely new algorithms that are being built now, it augments... >> Does it enhance master data, or extracts it away? What's the impact... like ClAIRE, for instance. What's the impact to MDM? More relevant, less relevant? >> Even more relevant, and three key areas of relevance. Number one is about automating the initial putting together about MDM, and then also automating the ongoing maintenance. Reacting to changes, both within the organization and outside the organization, and being able to learn from previous such interactions and making MDM self-configuring. The second part of it is stewardship. If you think about MDM, in the past you always had stewards, a small number of stewards in an organization who would go out and curate this data. We now have tens of thousands of businesses across the organization saying, "I want to interact with this master data, I have a role to play here." For those business users now, you have tens of thousands of them, and then thousands and thousands of attributes. Machine learning is the only way that you can stop this data explosion from causing a human explosion in terms of how do you manage this. >> John: Yeah, a meltdown. >> Yeah, a meltdown. MDM both is going to be improved through these technologies, but MDM also has to capture these crucial new sources of data and represent them to the business. >> New metadata, right? >> Yeah, all these artificial intelligence systems and machine learning stuff is going to be generating data that has to be captured somehow, and MDM's a crucial part of that. >> Exactly, right. >> So let me ask you a question. >> If we can boil this down really simply... >> John: He's excited about MDM. >> Look, I'm excited about data, this is so... If we kind of think about this, we had an accounting system, well let me step back. In the world where we were talking about hard assets, we had an accounting system that had a fixed asset module. So we put all our assets in there, we put depreciation schedules on it, we said, "Okay, who's got what? Who owns it, who owns the other things?" Is MDM really become the data asset system within the business? Is that too far a leap for you? >> I don't think so. I mean, if you think about, if master data was all about making sure that the business critical data, everything that the organization runs on, the business is running on, and now if you think of that, that's the data that's going to fuel, um, enable this digital disruption that these organizations want to do with that data, MDM's at the heart of that. And finally, the last piece I think, your point about the artificial intelligence, the third part of where MDM increases its relevance is, you have the insight now. The data is being put together, we've curated that data, we've discovered those relationships through machine learning. What next? What's next is really about not just putting that data in the hands of a user or inside of a consuming application, but instead, recommending what that application or user needs to do with that data. Predict what the next product is that a customer is going to buy, and make that next best offer recommendation to a system or a user. >> Suresh, you're the GM now, you've got the view of the landscape, you've got a business to run. Charge customers for the product, subscription, cloud, on-premise license, volving. You've got a new CMO. You've got to now snap into the storyline. What's your role in the storyline? Obviously, the story's got to be coherent around one big message and there's got to be the new logo we see behind here. What's your contribution to the story, and how are you guys keeping in cadence with the new marketing mission? >> This has been a very closely run project, this entire re-branding. It's not just a new logo and a new font for the company's name. This has been a process that began many, many months ago. It started from a look at what the direction of our products are across MDM. We worked very closely with Sally and her team to... >> John: So You've been involved. >> Absolutely, yes. >> The board certainly has. >> Both board members said they were actively involved as well. >> Yeah, this has been a... >> What do you think about it, are you excited? >> It's fantastic. >> It think it's one of those once-in-a-generation opportunities that we get where we've got such a broad breadth of capabilities across the company, and now to be able to tell that story in a way that we've never been able to before. >> It's going to help pull you into the wind that's blowing at your back. You guys have great momentum on the product site, congratulations. Now you got the... the brand is going to be building. >> Fantastic, yes. >> Okay, so what's the final question? Outlook for next year? How's the business going, you excited by things? >> Very much so. MDM has been across the board for Informatica, and I'm sure you've seen here at the conference, the interest in MDM, the success stories with MDM, large organizations like Coca-Cola and GE redoing the way they do business all powered through MDM. MDM has never been more relevant than it is now. >> And the data tsunami is here and coming and not stopping, the waves are hitting. IoT. Gene learning. >> Suresh: Right. >> Batching. >> Batching, absolutely. >> With enable frederated MDM, we'll be able to do this on a global scale, and master class... >> We'll have to have you come into our studio and do an MDM session. You guys are like, this is a great topic. Suresh, thank you so much for coming on theCUBE, really appreciate it. General Manager of the MDM Business for Informatica Master Data Management. Was once a cottage industry, now full blown, part of the data fabric at Informatica. Thanks so much for sharing on theCUBE. We're bringing you all the master CUBE interviews here in San Francisco for theCUBE's coverage of Informatica World. Back after this short break, stay with us. (techno music)

>> Narrator: Live from Austin, Texas it's theCUBE covering South by Southwest 2017 brought to you by Intel. Now here's John Furrier. Welcome back everyone, we are here live inside theCUBE SiliconANGLE Media's flagship program. We go out to events, and extract the signal from the noise, I'm John Furrier, we're here in the Intel AI Lounge for South by Southwest special, three days of coverage, interviews all day, some interviews tomorrow and some super demos and panels with Intel's top AI staff and thought leaders and experts and management. My next guest is Suresh Acharya with JDA Software, I've got it right? Welcome to theCUBE. >> Thank you. >> We were chatting before we were coming on about the IOT in your world, but you had made a comment about you were walking around the convention center-- >> Suresh: Yeah. >> What's it like outside? What's the scene look like out there? >> Well, I mean first of all, it's really fun to be here for South to Southwest, of course, and just walking from the convention center here, there are a lot of places, but you guys have something going on here, long lines, it's just a very, you know, a ... There's a huge buzz if you will. Very exciting. >> People are partying here, they got free beer, free booze-- >> Suresh: It's great! >> If you're watching and you're here at South pie, you definitely want to be at the Intel AI Lounge, one it's cooler, all the cool kids are here-- >> Suresh: That's right. Talking AI which is onstage, it's an AI VR show. You've seen a lot of virtual reality, you've seen a lot of AI. >> Suresh: Uh, huh. >> This speaks to a new interface, a new interface from a virtual augmented reality, but also AI from a data centric world-- >> Suresh: Of course. Yes. >> Your thoughts, cuz this is what you're involved in. >> Sure, let me tell you a little bit more about what I do, just to set the context. JDA we work in the supply chain and those are manufacturing plants into transportation into warehouse into stores. Things that are-- >> Known businesses, known processes. >> Known exactly. But what is now changing dramatically is the fact that a lot of this is being digitized. And not only is data being generated, the smarts, that's where the AI comes in has really helped or will continue to help improve efficiencies. So in your question around what the role of hollow ends or whatever the VR capabilities could be and where the smarts come in, if you will, is what we're trying to do is how do these technologies, how do you use them in the store, how do you use them in the warehouse, so that dynamically you can use the smarts for better efficiency. So that's where the machine learning as well as the VR technology comes together. >> So Suresh talk about the dynamics between data science and math and software, because what's happening is it's a real intersection now of confluence of maths, math and science, data, that's really available, and software. >> Suresh: Yeah. >> This is the power trend. This is the big tailwinds to the marketplace. >> Sure, so I'm a data scientist by training, you know I've always done algorithmic work and I've always worked in an industry where my mathematical models make it into the software. It's just music to my ears that a lot of this is now really, really becoming very, very important. Data science is just a word, there's two pieces. There's a data piece. There's a science piece. We all get trained in school on the science, and what we're finding early on was that data sometimes simply wasn't there. >> John: Yeah. >> But now, there's a lot more data, there's a lot more clean data and you can do a lot more with it. So it's a great time to be in AI, machine learning, and just the broader space of the data side. >> Well databases are changing, you're making more unstructured data available-- >> Suresh: Yes. >> Addressable, okay let's get back to your example of manufacturing in supply chain because I was going to say, boring, but it's never boring, it's business. >> Suresh: Yeah. >> We have a world we live in, an analog world, but you mentioned digitizing. This is not trivial. So I want you to take me through in your opinion and working in the labs of JDA Software, what are the key things for digitizing businesses, because you've got to bolt on senors, you got to have actuators, you got to have all kinds of new potentially hardware-- >> Suresh: Yeah. >> You need more processors. But now you got to connect it to the network, that's the Internet of Things. How hard is it to digitize a business? >> Sure, so it is hard and so this is more of a journey than something that's going to happen over night. Let me walk you through a couple of use cases both upstream to the end, and then the other way around, just so that you see the value and how complex, but yet how much value one can add. As you know, there are production plants all over the world, so it's quite possible then that there's a vessel that's carrying your product from China to Long Beach, California. A lot of times currently there's no visibility around when that ship will ever make it to Long Beach. But with sensors, with real-time tracking of all these vessels, we're now able to say that rather than it arriving in Long Beach on the 22nd because of weather reasons, it's now going to arrive on the 25th instead. And how that then drives the downstream supply chain around when should the product make it to the distribution center, when will it make it to the store, and oh, by the way, I might need to make alternate plans now because I don't have the luxury to wait for the three day delay that I am incurring, what are my alternate sources. So that's upstream down to the store. We don't really see it when we go buy something at the store, the fact that this has had such a long journey upstream, is typically shielded from us. >> So it's a ripple effect. >> Ripple effect. >> So the old days was, hey where's my product? Oh, it's on a boat from China, so you didn't know where it's coming from and the guild expression-- >> Suresh: Exactly. >> Maybe it was China or not. >> Suresh: Right. >> But the point was that you had a delay in impact, a disruption-- >> That's right. >> Here you can say, okay contingency policy, software, trigger, hey it's here, get some supply from somewhere else, it could be produce or other goods. >> Suresh: Exactly. >> Am I getting it right? >> You're absolutely right. So that's the kind of upstream down to the consumer, but how about the consumer or the store upstream, right, so sometimes what happens is folks go to the store and then they start to get on social media to say these are awesome products, everyone's got to buy em, these things start to sell off the shelf, if you will, very, very rapidly. And now can you start to detect that social sentiment trend to start to realign your supply chain so that you avoid out of stock. Alternatively, you could have the rewards-- >> Or you could game it like they're doing now. Create scarcity, then make the retail market move. >> There's that as well. >> Supreme is doing it. My kids are buying these things, Supreme, these jackets and backpacks. >> Correct. You can gamify as well. On the other hand, what you can also do is what if you introduce a new product, which you're now finding out is not selling as well as you thought it would. You're not going to continue to push inventory there, you're going to be smart about where you now send those and potentially also manage the manufacturing upstream. >> So it's the classic effect of efficiency opportunities are every. >> Suresh: Exactly. >> Talk how about Intel, what do you think Intel's doing right? Because if you think about about what's powering all this, it's the chips. >> Suresh: Yep. >> It's not just the processor and the PC, it's software end-to-end solutions. >> Suresh: Yeah. >> I was just covering Mobile World Congress two weeks ago, and 5G is bringing potentially a gigabit, I mean not that you need a sensor on a boat or a machine to use a gigabit-- >> Suresh: Sure. >> But still it does create more bandwidth-- >> Suresh: Yeah. >> Cuz you got to connect to the network. (laughs) >> Suresh: Sure. Exactly. (laughs) >> Your data's got to go somewhere. >> So one of the pieces of work that we're doing with Intel is really at the store level to have sensors detect where an object is. You'd be surprised. People sometimes, not sometimes a lot of times what happens is retailers will say that they're out of stock, when it's still in the store, it's just that they don't know where it is. >> John: Yeah. >> To now have sensors to precisely detect whether it's in the back office, whether it's in a fitting room, whether it's somewhere else and really track that inventory real-time to then provide the visibility around inventory is huge. This is the holy grail. You and I may not realize it, but this is the holy grail for a lot of retailers. Because they simply do not know where their inventory is and the work that we're doing around sensors, you know connecting the devices and of course adding the smarts with AI, that's the value. >> I love to hear the word holy grail, great stuff. I want to ask you a question on a personal note. >> Suresh: Yeah. >> Someone who's in labs and you've been in the industry of data science with a math background in retail, in supply chain, you kind of see the big picture. What are the coolest things out there right now, for the folks watching, whether it's a young kid or someone in college or an executive or a developer. Can you highlight some things of the coolest things that people should pay attention to, and what is cool that people aren't paying attention to. >> Yeah, well I think I'm going to be biased when I say just the space of machine learning is actually exploding, but it is. So that's my own heritage as well. To me it's just fascinating to see how things that were very rudimentary have now really caught on. So the area of AI and machine learning has endless potential in my mind. Around a lot of the devices then that actually generate the data that then feeds into it, that space is exploding as well. One of the pieces of work-- >> John: You mean IoT data? >> IoT data. I'd like to give you a specific example of things that are now possible. We are doing research in the space of cognitive robotics. These are not robots that will help automate things or make things faster, these are robots in the stores that will actually interact with you, so they will actually talk to you. You can go up it and say, "Hey, I'm trying to find "these shoes and I can't find them." What it's going to tell you is it's going to bring that immense power of AI to tell you where the products are, it could be in that store and it's going to have someone go fetch it for you, or it's going to tell you, oh it's in another store five miles down the road, would you rather go there to pick it up or it can say I can have it be mailed to your house. So that's in terms of the cognitive robot understanding your emotions that you're angry trying to find something or you're a happy customer and being able to respond that way, but it's also continuously collecting data about you. That it's a male of a certain age group coming into the store at this time, coming out of aisle number 19 looking for this kind of product. This is all pieces of info ... So our goal is even when you're 10 feet away from the robot, it's going to know what questions you're going to ask. >> So robotics is really hot right now, >> Suresh: Right. >> Because this is the interactivity potential, not just a static machine. >> Suresh: Correct. This is more ... >> It's the whole experience. >> We had Dr. Naveen, on earlier, Rao, he said it's like the Jetsons, go clean my room, I mean we're getting there. >> Suresh: We are getting there. >> Almost there. >> We're almost getting there and so ... So the notion that users will use software in a two-dimensional screen manner that we're doing now, that's already changing. So to your point earlier on VR being submersing yourself into your supply chain, which we never have done-- >> John: Yeah. >> Is really where this is going. >> John: Got it. >> So-- >> Suresh, so final question, shoot the arrow forward five years, what does our future look like, what's going to change, what's it going to look like? >> Well, there's a lot of buzz around the autonomous self driving car. In my world it's really the autonomous self-learning supply chain. Think about it, it's going to detect things, it's going to know things, it's going to predict things so much better and also be able to prescribe things dynamically. There's a lot of inefficiencies built into the supply chain that will gradually over time get better and better. So a lot of folks that could be scary, just like driverless car to a lot of folks is scary, but if you really grasp the value of it, where we're going is tremendous in terms of operational efficiencies, in terms of smart, just making our everyday lives so much better. >> Alright Suresh Acharya inside theCUBE, we're here in the Intel AI Lounge, I'm John Furrier with SiliconANGLE Media. We're breaking it down here at South by Southwest where all the buzz is happening virtual reality, artificial intelligence, machine learning is the hottest reality trend right now. Software developers are booming, it's Suresh great, it's the holy grail! This is theCUBE here at the Intel AI Lounge. Back with more coverage after this short break. (upbeat music)

(upbeat music) >> Welcome back to Supercloud22. This is an open community event, and it's dedicated to tracking the future of cloud in the 2020s. Supercloud is a term that we use to describe an architectural abstraction layer that hides the underlying complexities of the individual cloud primitives and APIs and creates a common experience for developers and users irrespective of where data is physically stored or on which cloud platform it lives. We're now going to explore the nuances of going to market in a world where data architectures span on premises across multiple clouds and are increasingly stretching out to the edge. Paula Hansen is the President and Chief Revenue Officer at Alteryx. And the reason we asked her to join us for Supercloud22 is because first of all, Alteryx is a company that is building a form of Supercloud in our view. If you have data in a bunch of different places and you need to pull in different data sets together, you might want to filter it or blend it, cleanse it, shape it, enrich it with other data, analyze it, report it out to your colleagues. Alteryx allows you to do that and automate that life cycle. And in our view is working to break down the data silos across clouds, hence Supercloud. Now, the other reason we invited Paula to the program is because she's a rockstar female in tech, and since day one at theCube, we've celebrated great women in tech, and in this case, a woman of data, Paula Hansen, welcome to the program. >> Thank you, Dave. I am absolutely thrilled to be here. >> Okay, we're going to focus on customers, their challenges and going to market in this cross cloud, multi-cloud, Supercloud world. First, Paula, what's changing in your view in the way that customers are innovating with data in the 2020s? >> Well, I think we've all learned very clearly over these last two years that the global pandemic has altered life and business as we know it. And now we're in an interesting time from a macroeconomic perspective as well. And so what we've seen is that every company in every industry has had to pivot and think about how they meet redefined customer expectations and an ever evolving competitive landscape. There really isn't an industry that wasn't reshaped in some way over the last couple of years. And we've been fortunate to work with companies in all industries that have adapted to this ever changing environment by leveraging Alteryx to help accelerate their digital transformations. Companies know that they need to unlock the full potential of their data to be able to move quickly to pivot and to respond to their customer's needs, as well as manage their businesses most efficiently. So I think nothing tells that story better than sharing a customer example with you, Dave. We love to share stories of our very innovative customers. And so the one that I'll share with you today in regards to this is Delta Airlines, who we're all very familiar with. And of course Delta's goal is to always keep their airplanes in the air flying passengers and getting people to their destinations efficiently. So they focus on the maintenance of their aircraft as a necessary part of running their business and they need to manage their maintenance stops and the maintenance of their aircraft very efficiently and effectively. So we work with them. They leverage our platform to automate all the processes for their aircraft maintenance centers. And so they've built out a fully automated reporting system on our platform leveraging tons of data. And this gives their service managers and their aircraft technicians foresight into what's happening with their scheduling and their maintenance processes. So this ensures that they've got the right technicians in the service center when the aircrafts land and that everything across that process is fully in place. And previously because of data silos and just complexity of data, this process would've taken them many many hours in each independent service center, and now leveraging Alteryx and the power of analytics and bringing all the data together. Those centers can do this process in just minutes and get their planes back in the air efficiently and delivering on their promises to their customers. So that's just one of many examples that we have in terms of the way the Alteryx analytics automation helps customers in this new age and helping to really unlock the power of their data. >> You know, Paul, that's an interesting example. Because in a previous life I worked with some airlines and people maybe don't realize this but, aircraft maintenance is the mission critical application for carriers. It's not the booking system. Because we've been there before, we show you there's a problem when you're booking or sometimes it's unfortunate, but people they get de booked. But the aircraft maintenance is the one that matters the most and that keeps planes in the air. So we hear all the time, you just mention it. About data silos and how problematic they are. So, specifically how are you seeing customers thinking about busting the data silos? >> Yeah, that's right, it's a big topic right now. Because companies realize that business processes that they run their business with, is very cross-functional in nature and requires data across every department in the enterprise. And you can't keep data locked in one department. So if you think of business processes like pay to procure or quote to cash, these are business processes that companies in every industry run their business. And that requires them to get data from multiple departments and bring all of that data together seamlessly to make the best business decisions that they can make. So what our platform does is, and is really well known for, is being very easy for users number one, and then number two, being really great at getting access to data quickly and easily from all those data silos, really, regardless of where it is. We talk about being everywhere. And when we say that we mean, whether it's on-prem, in your legacy applications and databases, or whether it's in the cloud with of course, all the multiple cloud platforms and modern cloud data warehouses. Regardless of where it is, we have the ability to bring that data together across hundreds of different data sources, bring it together to help drive insights and ultimately help our customers make better decisions, take action, and deliver on the business outcomes that they all are trying to drive within their respective industries. And what's- >> You know- >> Go ahead. >> Please carry on. >> Well, I was just going to say that what I do think has really sort of a tipping point in the last six months in particular is that executives themselves are really demanding of their organizations, this democratization of data. And the breaking down of the silos and empowering all of the employees across their enterprise regardless of how sophisticated they are with analytics to participate in the analytic opportunity. So we've seen some really cool things of late where executives, CEOs, chief financial officers, chief data officers are sponsoring events within their organizations to break down these silos and encourage their employees to come together on this democratization opportunity of democratization of data and analytics. And there's a shortage of data scientists on top of this. So there's no way that you're going to be able to hire enough data scientists to make sense of all this data running around your enterprise. So we believe with our platform we empower people regardless of their skillset. And so we see executives sponsoring these hackathons within their environments to bring together people to brainstorm and ideate on use cases, to share examples of how they leverage our platform and leverage the data within their organization to make better decisions. And it's really quite cool. Companies like Stanley Black & Decker, Ingersoll Rand, Inchcape PLC, these are all companies that the executive team has sponsored these hackathon events and seen really powerful things come out of it. As an example Ingersoll Rand sponsored their Alteryx hackathon with all of their data workers across various different functions where the data exists. And they focused on both top line revenue use cases as well as bottom line efficiency cases. And one of the outcomes was a use case that helped with their distribution center in north America and bringing all the data together across their various applications to reduce the amount of over ordering and under ordering of parts and more effectively manage their inventory within that distribution center. So, really cool to see this is now an executive level board level conversation. >> Very cool, a hackathon bringing people together for collaboration. A couple things that you said I want to comment on. Again, one of the reasons why we invited you guys to come on is, when you think about on-prem data and anybody who follows theCube and my breaking analysis program, knows we're big fans of Zhamak Dehghani's concept of data mesh. And data mesh is supposed to be inclusive. It doesn't matter if it's an S3 bucket, Oracle data base, or data warehouse, or data lake, that's just a note on the data mesh. And so it should be inclusive and Supercloud should include on-prem data to the extent that you can make that experience consistent. We have a lot of technical sessions here at Supercloud22, we're focusing now and go to market and the ecosystem. And we live in a world of multiple partners exploding ecosystems. And a lot of times it's co-opetition. So Paula, when you joined Alteryx you brought a proven go to market discipline to the company. Alignment with the customer, playbooks, best practice of sales, et cetera. And we've seen the results. It's a big reason why Mark Anderson and the board promoted you to president just after 10 months. Summarize how you approached the situation at Alteryx when you joined last spring. >> Yeah, I think first we were really intentional about what part of the market, what type of enterprises get the most benefit from the innovation that we deliver? And it's really clear that it's large enterprises. That the more complex a company is, most likely the more data they have and oftentimes the more decentralized that data is. And they're also really all trying to figure out how to remain competitive by leveraging that data. So, the first thing we did was be very intentional that we're focused on the enterprise and building out all of the capability required to be able to serve the enterprise. Of course, essential to all of that is having a platform capability because enterprises require that. So, with Suresh Vittal our Chief Product Officer, he's been fantastic in building out an end to end analytic platform that serves a wide range of analytic capabilities to a wide range of users. And then of course has this flexibility to operate both on-prem and in the cloud which is very important. Because we see this hybrid environment in this multicloud environment being something that is important to our customers. The second thing that I was really focused on was understanding how do you have those conversations with customers when they all are in maybe different types of backgrounds? So the way that you work with a business analyst in the office of finance or supply chain or sales and marketing, is different than the way that you serve a data scientist or a data engineer in IT. The way that you talk to a business owner who wants not to really understand the workflow level of data but wants to understand the insights of data, that's a different conversation. When you want to have a conversation of analytics for all or democratization of analytics at the executive level with the chief data officer or a CIO, that's a whole different conversation. And so we've built very specific sales plays to be able to have those conversations bring the relevant information to the relevant person so that we're really making sure that we explain the value proposition of the platform. Fully understand their world, their language and can work with them to deliver the value to them. And then the third thing that we did, was really heavily invest in our partnerships and you referenced this day. It's a a broad ecosystem out there. And we know that we have to integrate into that broad data ecosystem. and be a good partner to serve our customers. So, we've invested both in technology integration as well as go to market strategies with cloud data warehouse companies like Snowflake and Databricks, or RPA companies like UiPath and Blue Prism, as well as a wide range of other application and all of the cloud platforms because that's what our customers expect from us. So that's been a really important sort of third pillar of our strategy in making sure that from a go to market perspective, we understand where we fit in the ecosystem and how we collectively deliver on value to our joint customers. >> So that's super helpful. What I'm taking away from this is you didn't come to it with a generic playbook. Frank Lyman always talks about situation leadership. You assess the situation and applied that and a great example of partners is Snowflake and Databricks, these sort of opposites, but trying to solve similar problems. So you've got to be inclusive of all that. So we're trying to sort of squint through this Paula and say, okay, are there nuances and best practices beyond some of the the things that you just described that are unique to what we call Supercloud? Are there observations you can make with respect to what's different in this post isolation economy? Specifically in managing remote employees and of course remote partners, working with these complex ecosystems and the rise of this multi-cloud world, is it different or is it same wine new bottle? >> Well, I think it's both common from the on-prem or pre-cloud world, but there's also some differences as well. So what's common is that companies still expect innovation from us and still want us to be able to serve a wide range of skill sets. So our belief is that regardless of the skill set that you have, you can participate in the analytics opportunity for your company and unlocking the potential of your data. So we've been very focused since our inception to build out a platform that really serves this wide range of capabilities across the enterprise space. What's perhaps changed more or continues to evolve in this cloud world is just the flexibility that's required. You have to be everywhere. You have to be able to serve users wherever they are and be able to live in a multi-cloud or super cloud world. So when I think of cloud, I think it just unlocks a whole bigger opportunity for Alteryx and for companies that want to become analytic leaders. Because now you have users all over the globe, many of them looking for web-based analytic solutions. And of course these enterprises are all in various places on their journey to cloud and they want a partner and a platform that operates in all of those environments, which is what we do at Alteryx. So, I think it's an exciting time. I think that it's still very early in the analytic market and what companies are going to do to leverage their data to drive their transformation. And we're really excited to be a part of it. >> So last question is, I said up front we always like to celebrate women in tech. How'd you get into tech.? You've got a background, you've got somewhat of a technical background of being technical sales. And then of course rose up throughout your career and now have a leadership position. I called you a woman of data. How'd you get into it? Where'd you find the love of data? Give us the background and help us inspire some of the young women out there. >> Oh, well, but I'm super passionate about inspiring young women and thinking about the future next generation of women that can participate in technology and in data specifically. I grew up loving math and science. I went to school and got an electrical engineering degree but my passion around technology hasn't been just around technology for technology's sake, my passion around technology is what can it enable? What can it do? What are the outcomes that technology makes possible? And that's why data is so attractive because data makes amazing things possible. I shared some of those examples with you earlier but it not only can we have effect with data in businesses and enterprise, but governments globally now are realizing the ability for data to really have broad societal impact. And so I think that that speaks to women many times. Is that what does technology enable? What are the outcomes? What are the stories and examples that we can all share and be inspired by and feel good and and inspired to be a part of a broader opportunity that technology and data specifically enables? So that's what drives me. And those are the conversations that I have with the women that I speak with in all ages all the way down to K through 12 to inspire them to have a career in technology. >> Awesome, the more people in STEM the better, and the more women in our industry the better. Paula Hansen, thanks so much for coming in the program. Appreciate it. >> Thank you, Dave. >> Okay, keep it right there for more coverage from Supercloud 22, you're watching theCube. (upbeat music)

(upbeat music) >> Okay, we're kicking off the program with our first segment. Jay Henderson is the vice president of product management at Alteryx. And we're going to talk about the trends and data where we came from, how we got here, where we're going. We got some launch news. Hello, Jay, welcome to theCUBE. >> Great to be here. Really excited to share some of the things we're working on. >> Yeah, thank you. So look, you have a deep product background, product management, product marketing. You've done strategy work. You've been around software and data your entire career, and we're seeing the collision of software, data, cloud, machine intelligence. Let's start with the customer and maybe we can work back from there. So if you're an analytics or a data executive at an organization, Jay, what's your north star? Where are you trying to take your company from a data and analytics point of view? >> Yeah, I mean, look, I think all organizations are really struggling to get insights out of their data. I think one of the things that we see is you've got digital exhaust creating large volumes of data. Storage is really cheap, so it doesn't cost them much to keep it. And that results in a situation where the organization's drowning in data, but somehow still starving for insights. And so I think, you know, when I talk to customers, they're really excited to figure out how they can put analytics in the hands of every single person in their organization, and really start to democratize the analytics and you know, let the business users and the whole organization get value out of all that data they have. >> And we're going to dig into that throughout this program. And data, I like to say is plentiful. Insights, not always so much. Tell us about your launch today, Jay. And thinking about the trends that just highlighted, the direction that your customers want to go, and the problems that you're solving. What role does the cloud play, and what is what you're launching, how does that fit in? >> Yeah, we're really excited today we're launching the Alteryx analytics cloud. That's really a portfolio of cloud-based solutions that have all been built from the ground up to be cloud native, and to take advantage of things like browser based access. So that it's really easy to give anyone access including folks on a Mac. It also lets you take advantage of elastic compute, so that you can do, you know, in database processing and cloud native solutions that are going to scale to solve the most complex problems. So we've got a portfolio of solutions, things like designer cloud, which is our flagship designer product in a browser and on the cloud. We've got Alteryx machine learning which helps up-skill, regular, old analyst, with advanced machine learning capabilities. We've got auto insights, which brings business users into the fold and automatically unearths insights using AI and machine learning. And we've got our latest edition which is Trifacta, that helps data engineers do data pipelining, and really, you know, create a lot of the underlying data sets that are used in some of this downstream analytics. >> So let's dig into some of those roles, if we could a little bit. I mean, traditionally Alteryx has served the the business analysts, and that's what designer cloud is fit for, I believe. And you've explained kind of the scope. Sorry, you've expanded that scope into the to the business user with Hyper Anna. And in a moment, we're going to talk to Adam Wilson and Suresh, about Trifacta. And that recent acquisition takes you as you said into the data engineering space and IT, but in thinking about the business analyst role, what's unique about designer cloud and how does it help these individuals? >> Yeah, I mean, really I go back to some of the feedback we've had from our customers which is, you know, they oftentimes have dozens or hundreds of seats of our designer desktop product. Really as they look to take the next step, they're trying to figure out, how do I give access to that, those types of analytics to thousands of people within the organization. And designer cloud is really great for that. You've got the browser based interface. So if folks are on a Mac, they can really easily just pop open the browser and get access to all of those prep and blend capabilities to a lot of the analysis we're doing. It's a great way to scale up access to the analytics and start to put it in the hands of really anyone in the organization, not just those highly skilled power users. >> Okay, great. So now then you add in the Hyper Anna acquisition. So now you're targeting the business user, Trifacta comes into the mix, that deeper IT angle that we talked about. How does this all fit together? How should we be thinking about the new Alteryx portfolio? >> Yeah, I mean, I think it's pretty exciting. When you think about democratizing analytics and providing access to all these different groups of people, you've not been able to do it through one platform before. It's not going to be one interface that meets the needs of all these different groups within the organization, you really do need purpose built specialized capabilities for each group. And finally today with the announcement of the Alteryx analytics cloud, we brought together all of those different capabilities, all of those different interfaces into a single end to end application. So, really finally delivering on the promise of providing analytics to all. >> How much of this have you been able to share with your customers and maybe your partners? I mean, I know all this is fairly new but have you been able to get any feedback from them? What are they saying about it? >> Yeah, I mean, it's pretty amazing. We ran early access and limited availability program, that let us put a lot of this technology in the hands of over 600 customers. >> Oh, wow. >> Over the last few months. So we have gotten a lot of feedback. I tell you, it's been overwhelmingly positive. I think organizations are really excited to unlock the insights that have been hidden in all this data they've got. They're excited to be able to use analytics in every decision that they're making so that the decisions they have are more informed and produce better business outcomes. And this idea that they're going to move from, you know, dozens to hundreds or thousands of people who have access to these kinds of capabilities, I think has been a really exciting thing that is going to accelerate the transformation that these customers are on. >> That's good. Those are good numbers for a preview mode. Let's talk a little bit about vision. So if democratizing data is the ultimate goal, which frankly has been elusive for most organizations. Over time, how's your cloud going to address the challenges of putting data to work across the entire enterprise? >> Yeah, I mean, I tend to think about the future and some of the investments we're making in our products and our roadmap across four big themes. And these are really kind of enduring themes that you're going to see us making investments in over the next few years. The first is having cloud centricity. The data gravity has been moving to the cloud. We need to be able to provide access, to be able to ingest and manipulate that data, to be able to write back to it to provide cloud solutions. So, the first one is really around cloud centricity. The second is around big data fluency. Once you have all of that data you need to be able to manipulate it in a performant manner. So, having the elastic cloud infrastructure and in-database processing is so important. The third is around making AI a strategic advantage. So, you know, getting everyone involved in accessing AI and machine learning to unlock those insights, getting it out of the hands of the small group of data scientists, putting it in the hands of analysts and business users. And then the fourth thing is really providing access across the entire organization, IT and data engineers, as well as business owners and analysts. So, cloud centricity, big data fluency, AI as a strategic advantage, and personas across the organization, are really the the four big themes you're going to see us working on over the next few months and coming years. >> That's good, thank you for that. So on a related question, how do you see the data organizations evolving? I mean, traditionally you've had, you know monolithic organizations, very specialized, or I might even say hyper specialized roles. And your mission, of course, as the customer, you and your customers, they want to democratize the data. And so, it seems logical that domain leaders are going to take more responsibility for data life cycles, for data ownerships, low code becomes more important. And perhaps there's kind of challenges the historically highly centralized and really specialized roles that I just talked about. How do you see that evolving, and what role will Alteryx play? >> Yeah, I think we'll see sort of a more federated system start to emerge. Those centralized groups are going to continue to exist, but they're going to start to empower in a much more decentralized way, the people who are closer to the business problems and have better business understanding. I think that's going to let the centralized highly skilled teams work on problems that are of higher value to the organization. The kinds of problems where one or 2% lift in the model result in millions of dollars a day for the business. And then by pushing some of the analytics out closer to the edge and closer to the business, you'll be able to, you know, apply those analytics in every single decision. So I think you're going to see both the decentralized and centralized model start to work in harmony in a little bit more of a, almost a federated sort of way. And I think the exciting thing for us at Alteryx is, you know, we want to facilitate that. We want to give analytic capabilities and solutions to both groups and types of people. We want to help them collaborate better, and drive business outcomes with the analytics they're using. >> Yeah, I mean, I think my take on it, I wonder if you could comment is, to me the technology should be an operational detail. And it has been the dog that wags the tail or maybe the other way around. You mentioned digital exhaust before. I mean, essentially it's digital exhaust coming out of operational systems that then it somehow eventually end up in the hand of the domain users. And I wonder if increasingly we're going to see those domain users, those line of business experts get more access, that's your goal. And then even go beyond analytics, start to build data products that could be monitized. And that maybe it's going to take a decade to play out, but that is sort of a new era of data. Do you see it that way? >> Absolutely. We're actually making big investments in our products and capabilities to be able to create analytic applications, and to enable somebody who's an analyst or a business user to create an application on top of the data and analytics layers that they have, really to help democratize the analytics, to help pre-package some of the analytics that can drive more insights. So I think that's definitely a trend we're going to see more of. >> Yeah, and to your point, if you confederate the governance and automate that... >> Yep. Absolutely. >> Then that can happen. I mean, that's a key part of it, obviously, so... >> Yep. >> All right, Jay, we have to leave it there. Up next, we take a deep dive into the Alteryx recent acquisition of Trifacta with Adam Wilson, who led Trifacta for more than seven years, and Suresh Vittal, who is the chief product officer at Alteryx, to explain the rationale behind the acquisition, and how it's going to impact customers. Keep it right there. You're watching theCUBE, your leader in enterprise tech coverage. (upbeat music)